Pub Date : 2023-03-02DOI: 10.4015/s1016237222500478
Sonalee P. Suryawanshi, B. Dharmani
Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input thermogram images are accomplished by contrast enhancement and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) model is adopted for breast segmentation, and Optimized Fuzzy [Formula: see text]-Means Clustering (OFCM) is developed for abnormality segmentation. From the segmented region of interest, the entropy-based features are acquired. In the classification phase, the “Heuristic-based Support Vector Machine” (HSVM) and “Heuristic-based Neural Network” (HNN) are introduced, which diagnose the breast cancer-affected images. The modifications on SVM and NN are extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Furthermore, the suggested models are compared to the traditional SVM and NN classifiers, as well as other classifiers, to validate their competitive performance. From the results, the better accuracy and precision of the designed OI-TSA–HNN model are found to be 96% and 98.4%, respectively. Therefore, the findings confirm that the offered approach shows effectiveness in thermogram breast cancer detection.
{"title":"COMPARATIVE STUDY OF HEURISTIC-BASED SUPPORT VECTOR MACHINE AND NEURAL NETWORK FOR THERMOGRAM BREAST CANCER DETECTION WITH ENTROPY FEATURES","authors":"Sonalee P. Suryawanshi, B. Dharmani","doi":"10.4015/s1016237222500478","DOIUrl":"https://doi.org/10.4015/s1016237222500478","url":null,"abstract":"Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input thermogram images are accomplished by contrast enhancement and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) model is adopted for breast segmentation, and Optimized Fuzzy [Formula: see text]-Means Clustering (OFCM) is developed for abnormality segmentation. From the segmented region of interest, the entropy-based features are acquired. In the classification phase, the “Heuristic-based Support Vector Machine” (HSVM) and “Heuristic-based Neural Network” (HNN) are introduced, which diagnose the breast cancer-affected images. The modifications on SVM and NN are extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Furthermore, the suggested models are compared to the traditional SVM and NN classifiers, as well as other classifiers, to validate their competitive performance. From the results, the better accuracy and precision of the designed OI-TSA–HNN model are found to be 96% and 98.4%, respectively. Therefore, the findings confirm that the offered approach shows effectiveness in thermogram breast cancer detection.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"33 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78329984","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 : 2023-02-24DOI: 10.4015/s1016237223500035
S. Sharanya, S. Arjunan
Identifying Cardiac Autonomic Neuropathy (CAN) in the early stages of proliferation demands more prominent techniques with a reliable significance of identification. CAN being a subclinical consequence that is the leading cause of death in individuals with diabetes mellitus (DM), which is common among one in four people above an average age of 45 years, calls for a more dependable technique for analysis. This study investigates the complexity in prominent time segments (RR, QT and ST) of ECG using different entropy measures and four nonlinear fractal dimension (FD) measures including box counting, Petrosian, Higuchi’s and Katz’s methods. Measures of statistical significance were implemented using Wilcoxon, Mann–Whitney and Kruskal–Wallis tests. The results of the study provide an original approach to diagnostics that reveals the fact that, instead of analyzing the signal running for the whole length, complexity measures can be achieved, if the intervals of the signal are studied including a combination of features rather than any one feature considered for diagnosis. A significance level of [Formula: see text] is achieved in more segments of ECG considered at intervals of time compared to one data recorded at the 20th minute between CAN+ and CAN− groups for both FD and entropy. Neural Network (NN) classification shows the accuracies of 84.61% and 60% in FD and entropy, respectively, computed every fifth minute. The accuracies from the model for the data collected at the 20th minute for FD and entropy are 50.22% and 30.33%, respectively, between the groups.
{"title":"FRACTAL DIMENSION TECHNIQUES FOR ANALYSIS OF CARDIAC AUTONOMIC NEUROPATHY (CAN)","authors":"S. Sharanya, S. Arjunan","doi":"10.4015/s1016237223500035","DOIUrl":"https://doi.org/10.4015/s1016237223500035","url":null,"abstract":"Identifying Cardiac Autonomic Neuropathy (CAN) in the early stages of proliferation demands more prominent techniques with a reliable significance of identification. CAN being a subclinical consequence that is the leading cause of death in individuals with diabetes mellitus (DM), which is common among one in four people above an average age of 45 years, calls for a more dependable technique for analysis. This study investigates the complexity in prominent time segments (RR, QT and ST) of ECG using different entropy measures and four nonlinear fractal dimension (FD) measures including box counting, Petrosian, Higuchi’s and Katz’s methods. Measures of statistical significance were implemented using Wilcoxon, Mann–Whitney and Kruskal–Wallis tests. The results of the study provide an original approach to diagnostics that reveals the fact that, instead of analyzing the signal running for the whole length, complexity measures can be achieved, if the intervals of the signal are studied including a combination of features rather than any one feature considered for diagnosis. A significance level of [Formula: see text] is achieved in more segments of ECG considered at intervals of time compared to one data recorded at the 20th minute between CAN+ and CAN− groups for both FD and entropy. Neural Network (NN) classification shows the accuracies of 84.61% and 60% in FD and entropy, respectively, computed every fifth minute. The accuracies from the model for the data collected at the 20th minute for FD and entropy are 50.22% and 30.33%, respectively, between the groups.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77326351","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 : 2023-02-20DOI: 10.4015/s1016237222500491
K. Kanagalakshmi, Joycy K. Antony
Biometric authentication scheme is a robust, reliable, and convenient way for person authentication with security. It is necessary to protect biometric information for maintaining secrecy. In this paper, fingerprint template protection is carried out using the optimal iterative solubility (OIS) algorithm. The purpose of developing the OIS algorithm is to generate the matrix coefficient of the template protection matrix. The processing steps for fingerprint template protection involve two phases such as enrolment and authentication. In the enrolment phase, the identity vector of the input fingerprint image is generated with the assistance of minutiae points, secure point base (SPB) and OIS algorithm, and then, the database is created. In the authentication phase, the query image is considered as an input, and the identity vector is generated based on the query image in the same manner as enrolment phase. Moreover, the cross indexing-based matching is done using Tanimoto coefficient to make final decisions in order to check whether the user authorization is accepted or rejected. The experimental result demonstrates that the developed OIS algorithm attained a maximum accuracy of 0.96, minimum false acceptance rate (FAR) of 0.077, minimum false rejection rate (FRR) of 0.070, and maximum genuine acceptance rate (GAR) of 0.964, correspondingly.
{"title":"A CANCELLABLE AND IRREVOCABLE APPROACH FOR FINGERPRINT TEMPLATE PROTECTION USING OPTIMAL ITERATIVE SOLUBILITY ALGORITHM AND SECURE POINT BASE","authors":"K. Kanagalakshmi, Joycy K. Antony","doi":"10.4015/s1016237222500491","DOIUrl":"https://doi.org/10.4015/s1016237222500491","url":null,"abstract":"Biometric authentication scheme is a robust, reliable, and convenient way for person authentication with security. It is necessary to protect biometric information for maintaining secrecy. In this paper, fingerprint template protection is carried out using the optimal iterative solubility (OIS) algorithm. The purpose of developing the OIS algorithm is to generate the matrix coefficient of the template protection matrix. The processing steps for fingerprint template protection involve two phases such as enrolment and authentication. In the enrolment phase, the identity vector of the input fingerprint image is generated with the assistance of minutiae points, secure point base (SPB) and OIS algorithm, and then, the database is created. In the authentication phase, the query image is considered as an input, and the identity vector is generated based on the query image in the same manner as enrolment phase. Moreover, the cross indexing-based matching is done using Tanimoto coefficient to make final decisions in order to check whether the user authorization is accepted or rejected. The experimental result demonstrates that the developed OIS algorithm attained a maximum accuracy of 0.96, minimum false acceptance rate (FAR) of 0.077, minimum false rejection rate (FRR) of 0.070, and maximum genuine acceptance rate (GAR) of 0.964, correspondingly.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"39 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78042474","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 : 2023-02-08DOI: 10.4015/s1016237223500011
S. Tripathi, Neeraj Sharma
The early detection and treatment of COVID-19 infection are necessary to save human life. The study aims to propose a time-efficient and accurate method to classify lung infected images by COVID-19 and viral pneumonia using chest X-ray. The proposed classifier applies end-to-end training approach to classify the images of the set of normal, viral pneumonia and COVID-19-infected images. The features of the two infected classes were precisely captured by the extractor path and transferred to the constructor path for precise classification. The classifier accurately reconstructed the classes using the indices and the feature maps. For firm confirmation of the classification results, we used the Matthews correlation coefficient (MCC) along with accuracy and F1 scores (1 and 0.5). The classification accuracy of the COVID-19 class achieved was about ([Formula: see text])% with MCC score ([Formula: see text]). The classifier is distinguished with great precision between the two nearly correlated infectious classes (COVID-19 and viral pneumonia). The statistical test suggests that the obtained results are statistically significant as [Formula: see text]. The proposed method can save time in the diagnosis of lung infections and can help in reducing the burden on the medical system in the time of the pandemic.
{"title":"AUTOMATIC DETECTION OF COVID-19 AND VIRAL PNEUMONIA IN X-RAY IMAGES USING DEEP LEARNING APPROACH","authors":"S. Tripathi, Neeraj Sharma","doi":"10.4015/s1016237223500011","DOIUrl":"https://doi.org/10.4015/s1016237223500011","url":null,"abstract":"The early detection and treatment of COVID-19 infection are necessary to save human life. The study aims to propose a time-efficient and accurate method to classify lung infected images by COVID-19 and viral pneumonia using chest X-ray. The proposed classifier applies end-to-end training approach to classify the images of the set of normal, viral pneumonia and COVID-19-infected images. The features of the two infected classes were precisely captured by the extractor path and transferred to the constructor path for precise classification. The classifier accurately reconstructed the classes using the indices and the feature maps. For firm confirmation of the classification results, we used the Matthews correlation coefficient (MCC) along with accuracy and F1 scores (1 and 0.5). The classification accuracy of the COVID-19 class achieved was about ([Formula: see text])% with MCC score ([Formula: see text]). The classifier is distinguished with great precision between the two nearly correlated infectious classes (COVID-19 and viral pneumonia). The statistical test suggests that the obtained results are statistically significant as [Formula: see text]. The proposed method can save time in the diagnosis of lung infections and can help in reducing the burden on the medical system in the time of the pandemic.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85365841","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 : 2023-02-01DOI: 10.4015/s1016237222500533
R. Richa, U. Snekhalatha
Childhood obesity is a preventable disorder which can reduce the risk of the comorbidities linked with an adult obesity. In order to improve the lifestyle of the obese children, early and accurate detection is required by using some non-invasive technique. Thermal imaging helps in evaluation of childhood obesity without injecting any form of harmful radiation in human body. The goal of this proposed research is to evaluate the body surface temperature in abdominopelvic and cervical regions and to evaluate which region is best for predicting childhood obesity using thermal imaging. Next, to customize the ResNet-18 and VGG-19 architecture using transfer learning approach and to obtain the best modified classifier and to study the classification accuracy between normal and obese children. The two-study region which was selected for this study was abdominopelvic and cervical region where the mean skin surface temperature was recorded. From the two selected body regions, abdominopelvic region has depicted highest temperature difference of 10.98% between normal and obese subjects. The proposed modified ResNet-18 model produced an overall accuracy of 94.2% than the modified VGG-19 model (86.5%) for the classification of obese and normal children. Thus, this study can be considered as a non-invasive and cost-effective way for pre-screening the obesity condition in children.
{"title":"AUTOMATED DETECTION OF CHILDHOOD OBESITY IN ABDOMINOPELVIC REGION USING THERMAL IMAGING BASED ON DEEP LEARNING TECHNIQUES","authors":"R. Richa, U. Snekhalatha","doi":"10.4015/s1016237222500533","DOIUrl":"https://doi.org/10.4015/s1016237222500533","url":null,"abstract":"Childhood obesity is a preventable disorder which can reduce the risk of the comorbidities linked with an adult obesity. In order to improve the lifestyle of the obese children, early and accurate detection is required by using some non-invasive technique. Thermal imaging helps in evaluation of childhood obesity without injecting any form of harmful radiation in human body. The goal of this proposed research is to evaluate the body surface temperature in abdominopelvic and cervical regions and to evaluate which region is best for predicting childhood obesity using thermal imaging. Next, to customize the ResNet-18 and VGG-19 architecture using transfer learning approach and to obtain the best modified classifier and to study the classification accuracy between normal and obese children. The two-study region which was selected for this study was abdominopelvic and cervical region where the mean skin surface temperature was recorded. From the two selected body regions, abdominopelvic region has depicted highest temperature difference of 10.98% between normal and obese subjects. The proposed modified ResNet-18 model produced an overall accuracy of 94.2% than the modified VGG-19 model (86.5%) for the classification of obese and normal children. Thus, this study can be considered as a non-invasive and cost-effective way for pre-screening the obesity condition in children.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"13 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88629142","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":"Effect of cervical spine surgery on the biomechanics of the cervical spine","authors":"Jie Wang, Kevin X. Jiang, Hao Li","doi":"10.53388/bmec2023004","DOIUrl":"https://doi.org/10.53388/bmec2023004","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"41 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75430497","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":"CRISPR system contributes to deciphering the pharmaceutical compounds from TCM","authors":"Fan Wu, Guan Ju, Li-hong Zhou, Fu-Wen Yuan","doi":"10.53388/bmec2023008","DOIUrl":"https://doi.org/10.53388/bmec2023008","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83763592","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}
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