This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.
{"title":"On finite-time stability of some COVID-19 models using fractional discrete calculus","authors":"Shaher Momani , Iqbal M. Batiha , Issam Bendib , Abeer Al-Nana , Adel Ouannas , Mohamed Dalah","doi":"10.1016/j.cmpbup.2025.100188","DOIUrl":"10.1016/j.cmpbup.2025.100188","url":null,"abstract":"<div><div>This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-05-19DOI: 10.1016/j.cmpbup.2025.100193
Suleiman Daoud , Ahmad Nasayreh , Khalid M.O. Nahar , Wlla k. Abedalaziz , Salem M. Alayasreh , Hasan Gharaibeh , Ayah Bashkami , Amer Jaradat , Sultan Jarrar , Hammam Al-Hawamdeh , Absalom E. Ezugwu , Raed Abu Zitar , Aseel Smerat , Vaclav Snasel , Laith Abualigah
A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms such as KNN, Support Vector Machine SVM, and Random Forest (RF). The experimental results indicate that the proposed SWO+KNN model outperforms other well-established deep learning models and previous studies. SWO+KNN achieved accuracy rates of 97.5 % and 95.5 % for both binary classification and multiclass classification, respectively. The results clearly demonstrate the ability of the proposed SWO+KNN model to accurately classify brain tumors.
{"title":"A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction","authors":"Suleiman Daoud , Ahmad Nasayreh , Khalid M.O. Nahar , Wlla k. Abedalaziz , Salem M. Alayasreh , Hasan Gharaibeh , Ayah Bashkami , Amer Jaradat , Sultan Jarrar , Hammam Al-Hawamdeh , Absalom E. Ezugwu , Raed Abu Zitar , Aseel Smerat , Vaclav Snasel , Laith Abualigah","doi":"10.1016/j.cmpbup.2025.100193","DOIUrl":"10.1016/j.cmpbup.2025.100193","url":null,"abstract":"<div><div>A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms such as KNN, Support Vector Machine SVM, and Random Forest (RF). The experimental results indicate that the proposed SWO+KNN model outperforms other well-established deep learning models and previous studies. SWO+KNN achieved accuracy rates of 97.5 % and 95.5 % for both binary classification and multiclass classification, respectively. The results clearly demonstrate the ability of the proposed SWO+KNN model to accurately classify brain tumors.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retraction notice to ``Can digital vaccine passports potentially bring life back to “true-normal”?'' [Computer Methods and Programs in Biomedicine Update, Volume 1, (2021) 100011]","authors":"Fauzi Budi Satria , Mohamed Khalifa , Mihajlo Rabrenovic , Usman Iqbal","doi":"10.1016/j.cmpbup.2025.100203","DOIUrl":"10.1016/j.cmpbup.2025.100203","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-05-10DOI: 10.1016/j.cmpbup.2025.100191
Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri
Background
The issue is that most heart attacks and strokes happen unexpectedly to people who have signs of high blood pressure that are not identified in time for treatment. These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen.
Methods
This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. This database comprises 1200 retinal images, each measuring 1440 × 940 pixels. The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). This stage aims to extract and sample the comparison between the width of the artery and vein (AVR). This research uses a Deep Convolutional Neural Network (DCNN) classification model with cross-validation training using the Leave-one-out method.
Results
The results of testing the model with nine output classes, the features extracted in each convolutional layer, the second layer successfully extracts the retina and eye blood vessels, the third layer extracts the retinal image texture, and the fourth layer extracts hard exudates, hemorrhages, and cotton wool spots. Meanwhile, the Specificity, Recall, Accuracy, and F-Score results are 90 %, 81.82 %, 90 %, and 90 %, respectively.
Conclusions
This research's findings first include applying the AVR calculation algorithm to build a new dataset with 9 class categories. Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.
{"title":"Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach","authors":"Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri","doi":"10.1016/j.cmpbup.2025.100191","DOIUrl":"10.1016/j.cmpbup.2025.100191","url":null,"abstract":"<div><h3>Background</h3><div>The issue is that most heart attacks and strokes happen unexpectedly to people who have signs of high blood pressure that are not identified in time for treatment. These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen.</div></div><div><h3>Methods</h3><div>This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. This database comprises 1200 retinal images, each measuring 1440 × 940 pixels. The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). This stage aims to extract and sample the comparison between the width of the artery and vein (AVR). This research uses a Deep Convolutional Neural Network (DCNN) classification model with cross-validation training using the Leave-one-out method.</div></div><div><h3>Results</h3><div>The results of testing the model with nine output classes, the features extracted in each convolutional layer, the second layer successfully extracts the retina and eye blood vessels, the third layer extracts the retinal image texture, and the fourth layer extracts hard exudates, hemorrhages, and cotton wool spots. Meanwhile, the Specificity, Recall, Accuracy, and F-Score results are 90 %, 81.82 %, 90 %, and 90 %, respectively.</div></div><div><h3>Conclusions</h3><div>This research's findings first include applying the AVR calculation algorithm to build a new dataset with 9 class categories. Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-10DOI: 10.1016/j.cmpbup.2025.100201
Zafar Iqbal , Nauman Ahmed , Abid Ali , Ali Raza , Muhammad Rafiq , Ilyas Khan
In this work, the effects and propagation of smoking in society are studied by considering the fractional tobacco smoking model. For this reason, the underlying model is investigated both analytically and numerically. The system has two equilibrium points, namely the tobacco-free and endemic equilibrium points. Furthermore, the stability of the model is observed by applying the Jacobian matrix technique. For numerical study, the non-standard finite difference scheme (NSFD) is hybridized with the Grunwald-Letnikov (GL) approximation for the Caputo differential operator. The key features of the continuous model are examined for the projected GL-NSFD scheme. The numerically simulated graphs are plotted to guarantee the positivity, boundedness, and convergence towards the exact steady states. Since the integer order epidemic model cannot accurately capture the nonlinear real phenomenon. Moreover, they cannot predict the future state exactly as the integer order derivatives involved in the models are local by nature, and they do not have the memory effect or history of the system. On the contrary, the fractional order model can capture all the necessary features of the continuous model. The proposed numerical method preserves the structure of the continuous system, for instance, the positivity, boundedness and convergence toward the exact steady states. It is worth mentioning that the projected numerical scheme is consistent with the continuous system.
{"title":"Numerical modelling and stability analysis of fractional smoking model","authors":"Zafar Iqbal , Nauman Ahmed , Abid Ali , Ali Raza , Muhammad Rafiq , Ilyas Khan","doi":"10.1016/j.cmpbup.2025.100201","DOIUrl":"10.1016/j.cmpbup.2025.100201","url":null,"abstract":"<div><div>In this work, the effects and propagation of smoking in society are studied by considering the fractional tobacco smoking model. For this reason, the underlying model is investigated both analytically and numerically. The system has two equilibrium points, namely the tobacco-free and endemic equilibrium points. Furthermore, the stability of the model is observed by applying the Jacobian matrix technique. For numerical study, the non-standard finite difference scheme (NSFD) is hybridized with the Grunwald-Letnikov (GL) approximation for the Caputo differential operator. The key features of the continuous model are examined for the projected GL-NSFD scheme. The numerically simulated graphs are plotted to guarantee the positivity, boundedness, and convergence towards the exact steady states. Since the integer order epidemic model cannot accurately capture the nonlinear real phenomenon. Moreover, they cannot predict the future state exactly as the integer order derivatives involved in the models are local by nature, and they do not have the memory effect or history of the system. On the contrary, the fractional order model can capture all the necessary features of the continuous model. The proposed numerical method preserves the structure of the continuous system, for instance, the positivity, boundedness and convergence toward the exact steady states. It is worth mentioning that the projected numerical scheme is consistent with the continuous system.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-08-20DOI: 10.1016/j.cmpbup.2024.100161
Mohamed Khalifa , Farah Magrabi , Blanca Gallego
Background
When selecting clinical predictive tools, clinicians are challenged with an overwhelming and ever-growing number, most of which have never been implemented or evaluated for effectiveness. The authors developed an evidence-based framework for grading and assessment of predictive tools (GRASP). The objective of this study is to refine, validate GRASP, and assess its reliability for consistent application.
Methods
A mixed-methods study was conducted, involving an initial web-based survey for feedback from a wide group of international experts in clinical prediction to refine the GRASP framework, followed by reliability testing with two independent researchers assessing eight predictive tools. The survey involved 81 experts who rated agreement with the framework's criteria on a five-point Likert scale and provided qualitative feedback. The reliability of the GRASP framework was evaluated through interrater reliability testing using Spearman's rank correlation coefficient.
Results
The survey yielded strong agreement of the experts with the framework's evaluation criteria, overall average score: 4.35/5, highlighting the importance of predictive performance, usability, potential effect, and post-implementation impact in grading clinical predictive tools. Qualitative feedback led to significant refinements, including detailed categorisation of evidence levels and clearer representation of evaluation criteria. Interrater reliability testing showed high agreement between researchers and authors (0.994) and among researchers (0.988), indicating strong consistency in tool grading.
Conclusion
The GRASP framework provides a high-level, evidence-based, and comprehensive, yet simple and feasible, approach to evaluate, compare, and select the best clinical predictive tools, with strong expert agreement and high interrater reliability. It assists clinicians in selecting effective tools by grading them on the level of validation of predictive performance before implementation, usability and potential effect during planning for implementation, and post-implementation impact on healthcare processes and clinical outcomes. Future studies should focus on the framework's application in clinical settings and its impact on decision-making and guideline development.
{"title":"Validating and updating GRASP: An evidence-based framework for grading and assessment of clinical predictive tools","authors":"Mohamed Khalifa , Farah Magrabi , Blanca Gallego","doi":"10.1016/j.cmpbup.2024.100161","DOIUrl":"10.1016/j.cmpbup.2024.100161","url":null,"abstract":"<div><h3>Background</h3><div>When selecting clinical predictive tools, clinicians are challenged with an overwhelming and ever-growing number, most of which have never been implemented or evaluated for effectiveness. The authors developed an evidence-based framework for grading and assessment of predictive tools (GRASP). The objective of this study is to refine, validate GRASP, and assess its reliability for consistent application.</div></div><div><h3>Methods</h3><div>A mixed-methods study was conducted, involving an initial web-based survey for feedback from a wide group of international experts in clinical prediction to refine the GRASP framework, followed by reliability testing with two independent researchers assessing eight predictive tools. The survey involved 81 experts who rated agreement with the framework's criteria on a five-point Likert scale and provided qualitative feedback. The reliability of the GRASP framework was evaluated through interrater reliability testing using Spearman's rank correlation coefficient.</div></div><div><h3>Results</h3><div>The survey yielded strong agreement of the experts with the framework's evaluation criteria, overall average score: 4.35/5, highlighting the importance of predictive performance, usability, potential effect, and post-implementation impact in grading clinical predictive tools. Qualitative feedback led to significant refinements, including detailed categorisation of evidence levels and clearer representation of evaluation criteria. Interrater reliability testing showed high agreement between researchers and authors (0.994) and among researchers (0.988), indicating strong consistency in tool grading.</div></div><div><h3>Conclusion</h3><div>The GRASP framework provides a high-level, evidence-based, and comprehensive, yet simple and feasible, approach to evaluate, compare, and select the best clinical predictive tools, with strong expert agreement and high interrater reliability. It assists clinicians in selecting effective tools by grading them on the level of validation of predictive performance before implementation, usability and potential effect during planning for implementation, and post-implementation impact on healthcare processes and clinical outcomes. Future studies should focus on the framework's application in clinical settings and its impact on decision-making and guideline development.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2024.100173
Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan
Chronic Kidney Disease (CKD), the gradual loss and irreversible damage of the kidney’s functionality, is one of the leading contributors to death and causes about 1.3 million people to die annually. It is extremely important to slow down the progression of kidney deterioration to prevent kidney dialysis or transplant. This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. Two encoding techniques are introduced to combine the datasets, i.e., Discrete Encoding and Ranged Encoding, resulting in Discrete Merged and Ranged Merged datasets. The preprocessing stage employs normalization, class balancing with synthetic oversampling, and five feature selection techniques, including RFECV and Pearson Correlation. This work proposes a novel Tri-phase Ensemble technique combining Voting, Bagging, and Stacking approaches and two other ensemble models: Multi-layer Stacking and Multi-layer Blending classifiers. The investigation reveals that, for the Discrete Merged dataset, the novel Tri-phase Ensemble and Multi-layer Stacking with layers interchanged achieves an accuracy of 99.5%. For the Ranged Merged dataset, AdaBoost attains an accuracy of 97.5%. Logistic Regression accomplishes an accuracy of 99.5% in validating with the discrete dataset, whereas for validating with the ranged dataset, both Random Forest and SVM achieve 100% accuracy. Finally, to interpret and understand the behavior and prediction of the model, a LIME explainer has been utilized.
{"title":"ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach","authors":"Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan","doi":"10.1016/j.cmpbup.2024.100173","DOIUrl":"10.1016/j.cmpbup.2024.100173","url":null,"abstract":"<div><div>Chronic Kidney Disease (CKD), the gradual loss and irreversible damage of the kidney’s functionality, is one of the leading contributors to death and causes about 1.3 million people to die annually. It is extremely important to slow down the progression of kidney deterioration to prevent kidney dialysis or transplant. This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. Two encoding techniques are introduced to combine the datasets, i.e., Discrete Encoding and Ranged Encoding, resulting in Discrete Merged and Ranged Merged datasets. The preprocessing stage employs normalization, class balancing with synthetic oversampling, and five feature selection techniques, including RFECV and Pearson Correlation. This work proposes a novel Tri-phase Ensemble technique combining Voting, Bagging, and Stacking approaches and two other ensemble models: Multi-layer Stacking and Multi-layer Blending classifiers. The investigation reveals that, for the Discrete Merged dataset, the novel Tri-phase Ensemble and Multi-layer Stacking with layers interchanged achieves an accuracy of 99.5%. For the Ranged Merged dataset, AdaBoost attains an accuracy of 97.5%. Logistic Regression accomplishes an accuracy of 99.5% in validating with the discrete dataset, whereas for validating with the ranged dataset, both Random Forest and SVM achieve 100% accuracy. Finally, to interpret and understand the behavior and prediction of the model, a LIME explainer has been utilized.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-01-20DOI: 10.1016/j.cmpbup.2025.100178
Edwiga Renald , Miracle Amadi , Heikki Haario , Joram Buza , Jean M. Tchuenche , Verdiana G. Masanja
The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease.
{"title":"A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model","authors":"Edwiga Renald , Miracle Amadi , Heikki Haario , Joram Buza , Jean M. Tchuenche , Verdiana G. Masanja","doi":"10.1016/j.cmpbup.2025.100178","DOIUrl":"10.1016/j.cmpbup.2025.100178","url":null,"abstract":"<div><div>The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of artificial intelligence (AI) into clinical decision-making has introduced new opportunities for automating and enhancing medical documentation, particularly in oncology, where multidisciplinary meetings are central to treatment planning. However, existing speech-to-text and retrieval-augmented generation (RAG) systems are not equipped to operate effectively in multilingual, dialect-rich environments such as those in North African hospitals where Moroccan Darija, Arabic, and French are frequently interwoven. These linguistic complexities, combined with the high-stakes nature of clinical dialogue, challenge transcription accuracy, contextual information retrieval, and regulatory compliance. This study presents a multilingual RAG system tailored to clinical meetings, integrating a fine-tuned Whisper ASR model with a sentence-level semantic retrieval pipeline and a compliance-aware generation framework. Evaluated on real-world clinical queries, the system demonstrates improved transcription quality and retrieval precision over standard pipelines, while enforcing factual grounding and safety through multi-stage output validation. These results highlight the potential of multilingual, speech-driven AI to support decision-making and compliance in linguistically diverse healthcare environments, offering a deployable foundation for clinical NLP in underserved regions.
{"title":"Leveraging multilingual RAG for breast cancer RCPs: AI-driven speech transcription and compliance in Darija-French clinical discussions","authors":"Ilyass Emssaad , Fatima-Ezzahraa Ben-Bouazzaa , Idriss Tafala , Manal Chakour El Mezali , Bassma Jioudi","doi":"10.1016/j.cmpbup.2025.100221","DOIUrl":"10.1016/j.cmpbup.2025.100221","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) into clinical decision-making has introduced new opportunities for automating and enhancing medical documentation, particularly in oncology, where multidisciplinary meetings are central to treatment planning. However, existing speech-to-text and retrieval-augmented generation (RAG) systems are not equipped to operate effectively in multilingual, dialect-rich environments such as those in North African hospitals where Moroccan Darija, Arabic, and French are frequently interwoven. These linguistic complexities, combined with the high-stakes nature of clinical dialogue, challenge transcription accuracy, contextual information retrieval, and regulatory compliance. This study presents a multilingual RAG system tailored to clinical meetings, integrating a fine-tuned Whisper ASR model with a sentence-level semantic retrieval pipeline and a compliance-aware generation framework. Evaluated on real-world clinical queries, the system demonstrates improved transcription quality and retrieval precision over standard pipelines, while enforcing factual grounding and safety through multi-stage output validation. These results highlight the potential of multilingual, speech-driven AI to support decision-making and compliance in linguistically diverse healthcare environments, offering a deployable foundation for clinical NLP in underserved regions.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-12-07DOI: 10.1016/j.cmpbup.2024.100171
Rutwik Gulakala, Marcus Stoffel
Background and objective:
In the diagnosis of medical images, neural network classifications can support rapid diagnosis together with existing imaging methods. Although current state-of-the-art deep learning methods can contribute to this image recognition, the aim of the present study is to develop a general classification framework with brain-inspired neural networks. Following this intention, spiking neural network models, also known as third-generation models, are included here to capitalize on their sparse characteristics and capacity to significantly decrease energy consumption. Inspired by the recent development of neuromorphic hardware, a sustainable neural network framework is proposed, leading to an energy reduction down to a thousandth compared to the current state-of-the-art second-generation counterpart of artificial neural networks. Making use of sparse signal transmissions as in the human brain, a neuromorphic algorithm for imaging diagnostics is introduced.
Methods:
A novel, sustainable, brain-inspired spiking neural network is proposed to perform the multi-class classification of digital medical images. The framework comprises branched and densely connected layers described by a Leaky-Integrate and Fire (LIF) neuron model. Backpropagation of discontinuous spiking activations in the forward pass is achieved by surrogate gradients, in this case, fast sigmoid. The data for the spiking neural network is encoded into binary spikes with a latency encoding strategy. The proposed model is evaluated on a publicly available dataset of digital X-rays of chest and compared with an equivalent classical neural network. The models are trained using enhanced and pre-processed X-ray images and are evaluated based on classification metrics.
Results:
The proposed neuromorphic framework had an extremely high classification accuracy of 99.22 on an unseen test set, together with high precision and recall figures. The framework achieves this accuracy, all the while consuming 1000 times less electrical power than classical neural network architectures.
Conclusion:
Though there is a loss of information due to encoding, the proposed neuromorphic framework has achieved accuracy close to its second-generation counterpart. Therefore, the benefit of the proposed framework is the high accuracy of classification while consuming a thousandth of the power, enabling a sustainable and accessible add-on for the available diagnostic tools, such as medical imaging equipment, to achieve rapid diagnosis.
背景与目的：在医学图像诊断中，神经网络分类可以与现有的成像方法一起支持快速诊断。虽然目前最先进的深度学习方法可以为这种图像识别做出贡献，但本研究的目的是利用脑启发神经网络开发一种通用分类框架。根据这一意图，这里采用了尖峰神经网络模型（也称为第三代模型），以利用其稀疏特性和能力来显著降低能耗。受最近神经形态硬件发展的启发，我们提出了一种可持续的神经网络框架，与目前最先进的第二代人工神经网络相比，能耗降低了千分之一。方法：提出了一种新型、可持续、受大脑启发的尖峰神经网络，用于执行数字医学图像的多级分类。该框架由分支层和密集连接层组成，这些层由泄漏-整合-发射（LIF）神经元模型描述。前向传递中不连续尖峰激活的反向传播是通过替代梯度实现的，在本例中是快速西格玛梯度。尖峰神经网络的数据通过延迟编码策略编码为二进制尖峰。我们在一个公开的胸部数字 X 光片数据集上对所提出的模型进行了评估，并将其与等效的经典神经网络进行了比较。结果：所提出的神经形态框架在未见测试集上的分类准确率高达 99.22%，而且精确度和召回率也很高。结论：虽然编码会造成信息损失，但所提出的神经形态框架达到了接近第二代框架的准确度。因此，所提框架的优势在于分类准确度高，而功耗仅为传统神经网络架构的千分之一，可为现有诊断工具（如医疗成像设备）提供可持续、可访问的附加功能，实现快速诊断。
{"title":"A sustainable neuromorphic framework for disease diagnosis using digital medical imaging","authors":"Rutwik Gulakala, Marcus Stoffel","doi":"10.1016/j.cmpbup.2024.100171","DOIUrl":"10.1016/j.cmpbup.2024.100171","url":null,"abstract":"<div><h3>Background and objective:</h3><div>In the diagnosis of medical images, neural network classifications can support rapid diagnosis together with existing imaging methods. Although current state-of-the-art deep learning methods can contribute to this image recognition, the aim of the present study is to develop a general classification framework with brain-inspired neural networks. Following this intention, spiking neural network models, also known as third-generation models, are included here to capitalize on their sparse characteristics and capacity to significantly decrease energy consumption. Inspired by the recent development of neuromorphic hardware, a sustainable neural network framework is proposed, leading to an energy reduction down to a thousandth compared to the current state-of-the-art second-generation counterpart of artificial neural networks. Making use of sparse signal transmissions as in the human brain, a neuromorphic algorithm for imaging diagnostics is introduced.</div></div><div><h3>Methods:</h3><div>A novel, sustainable, brain-inspired spiking neural network is proposed to perform the multi-class classification of digital medical images. The framework comprises branched and densely connected layers described by a Leaky-Integrate and Fire (LIF) neuron model. Backpropagation of discontinuous spiking activations in the forward pass is achieved by surrogate gradients, in this case, fast sigmoid. The data for the spiking neural network is encoded into binary spikes with a latency encoding strategy. The proposed model is evaluated on a publicly available dataset of digital X-rays of chest and compared with an equivalent classical neural network. The models are trained using enhanced and pre-processed X-ray images and are evaluated based on classification metrics.</div></div><div><h3>Results:</h3><div>The proposed neuromorphic framework had an extremely high classification accuracy of 99.22<span><math><mtext>%</mtext></math></span> on an unseen test set, together with high precision and recall figures. The framework achieves this accuracy, all the while consuming 1000 times less electrical power than classical neural network architectures.</div></div><div><h3>Conclusion:</h3><div>Though there is a loss of information due to encoding, the proposed neuromorphic framework has achieved accuracy close to its second-generation counterpart. Therefore, the benefit of the proposed framework is the high accuracy of classification while consuming a thousandth of the power, enabling a sustainable and accessible add-on for the available diagnostic tools, such as medical imaging equipment, to achieve rapid diagnosis.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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