Pub Date : 2023-07-07DOI: 10.1142/s0219467824500578
G. Ashwini, T. Ramashri, Mohammad Rasheed Ahmed
The prominence and popularity of Image Denoising in medical image processing has been obvious since its early conception. Medical Image Denoising is primarily a significant pre-processing method for further image processing steps in various fields. Its ability to speed up the diagnosis by enhancing the sensory quality of noisy images is proven to be working in most of the cases. The efficiency of the deep neural networks for Medical Image Denoising has been well proven traditionally. Both noisy and clean images are equal requirements in most of these training methods. However, it is not always possible to procure clean images for various applications such as Dynamic Imaging, Computed Tomography, Magnetic Resonance Imaging, and Camera Photography due to the inevitable presence of naturally occurring noisy signals which are intrinsic to the images. There have been self-supervised single Image Denoising methods proposed recently. Being inspired by these methods, taking this a step further, we propose a novel and better denoising method for single images by training the learning model on each of the channels of the input data, which is termed as “Noise2Split”. It ultimately proves to reduce the noise granularly in each channel, pixel by pixel, by using Single Channeled Patch-Based (SCPB) learning, which is found to be resulting in a better performance. Further, to obtain optimum results, the method leverages BRISQUE image quality assessment. The model is demonstrated on X-ray, CT, PET, Microscopy, and real-world noisy images.
{"title":"Noise2Split — Single Image Denoising Via Single Channeled Patch-Based Learning","authors":"G. Ashwini, T. Ramashri, Mohammad Rasheed Ahmed","doi":"10.1142/s0219467824500578","DOIUrl":"https://doi.org/10.1142/s0219467824500578","url":null,"abstract":"The prominence and popularity of Image Denoising in medical image processing has been obvious since its early conception. Medical Image Denoising is primarily a significant pre-processing method for further image processing steps in various fields. Its ability to speed up the diagnosis by enhancing the sensory quality of noisy images is proven to be working in most of the cases. The efficiency of the deep neural networks for Medical Image Denoising has been well proven traditionally. Both noisy and clean images are equal requirements in most of these training methods. However, it is not always possible to procure clean images for various applications such as Dynamic Imaging, Computed Tomography, Magnetic Resonance Imaging, and Camera Photography due to the inevitable presence of naturally occurring noisy signals which are intrinsic to the images. There have been self-supervised single Image Denoising methods proposed recently. Being inspired by these methods, taking this a step further, we propose a novel and better denoising method for single images by training the learning model on each of the channels of the input data, which is termed as “Noise2Split”. It ultimately proves to reduce the noise granularly in each channel, pixel by pixel, by using Single Channeled Patch-Based (SCPB) learning, which is found to be resulting in a better performance. Further, to obtain optimum results, the method leverages BRISQUE image quality assessment. The model is demonstrated on X-ray, CT, PET, Microscopy, and real-world noisy images.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46915734","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 : 2023-06-30DOI: 10.1142/s0219467824500414
R. V. Prasad, J. Prasad, B. Chaudhari, Nihar M. Ranjan, Rajat Srivastava
Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.
洪水是致命的灾难性灾害，会造成生命损失和财产、农田和基础设施的破坏。为了解决这个问题，有必要设计和采用一个有效的洪水管理系统，可以立即识别洪水区域，并尽快采取救援措施。因此，本研究开发了一种有效的洪水检测方法——基于反电晕洗牌牧羊人优化算法的深度量子神经网络(ACSSOA-based Deep Quantum Neural Network，简称Deep QNN)来识别洪水泛滥区域。在这里，使用空间约束多核距离模糊c均值(MKFCM_S)进行分割过程，其中模糊c均值(FCM)使用基于核诱导距离的空间约束(KFCM_S)进行修改。对于洪水检测，已经使用了深度QNN，其中深度QNN的训练过程是使用设计的优化算法ACSSOA完成的。此外，所设计的ACSSOA是由抗冠状病毒优化算法(ACVO)和shuffle Shepherd优化算法(SSOA)杂交而成的。利用喀拉拉邦洪水数据库对该方法进行了评价，结果表明，该方法的分割精度、检测精度、灵敏度和特异性最高，分别为0.904、0.914、0.927和0.920。
{"title":"FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection","authors":"R. V. Prasad, J. Prasad, B. Chaudhari, Nihar M. Ranjan, Rajat Srivastava","doi":"10.1142/s0219467824500414","DOIUrl":"https://doi.org/10.1142/s0219467824500414","url":null,"abstract":"Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45182967","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 : 2023-06-07DOI: 10.1142/s0219467824500451
M. Prashanthi, M. Chandra Mohan
The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.
{"title":"Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction","authors":"M. Prashanthi, M. Chandra Mohan","doi":"10.1142/s0219467824500451","DOIUrl":"https://doi.org/10.1142/s0219467824500451","url":null,"abstract":"The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49066729","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 : 2023-06-05DOI: 10.1142/s0219467824500384
K. P. Kumar, M. Rao, M. Venkatanarayana
Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.
{"title":"A Novel Image Recovery from Moving Water Surface Using Multi-Objective Bispectrum Method","authors":"K. P. Kumar, M. Rao, M. Venkatanarayana","doi":"10.1142/s0219467824500384","DOIUrl":"https://doi.org/10.1142/s0219467824500384","url":null,"abstract":"Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45247148","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 : 2023-05-22DOI: 10.1142/s0219467824500360
Fuxiang Liu, Chen Zang, Junqi Shi, Weiyu He, Yubo Liang, Lei Li
Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.
{"title":"An Improved COVID-19 Lung X-Ray Image Classification Algorithm Based on ConvNeXt Network","authors":"Fuxiang Liu, Chen Zang, Junqi Shi, Weiyu He, Yubo Liang, Lei Li","doi":"10.1142/s0219467824500360","DOIUrl":"https://doi.org/10.1142/s0219467824500360","url":null,"abstract":"Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43948823","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 : 2023-05-05DOI: 10.1142/s021946782450044x
K. Visalini, Saravanan Alagarsamy, S. Raja
Studies deem that about 1 percent of the human population is affected by epileptic seizures on a global scale. It is characterized as an undue neuronal discharge in the brain and degrades the quality of life of the patients to a large extent. Children being unaware of a sudden onset of seizures could be affected by severe injury or even mortality. Machine-learning-based epileptic seizure detection from EEG (Electro-Encephalogram) signals have always been a hot area of research. However, the majority of the research works rely on correlated non-linear features extracted from the EEG signals, causing a high-computational overhead, and challenging their application in real-time clinical diagnosis. This study proposes a robust seizure detection framework using Gaussian Deep Boltzmann Machine-based classifier and Symplectic Geometric Decomposition (SGD)-based features. The simplified eigenvalues derived through Symplectic Similarity Transform (SST) are employed as feature vectors for the classifier, eliminating the need for a deliberate feature extraction procedure. The study examines the transferability capability of the suggested framework in discriminating seizures in both neonates and pediatric subjects in unison, experimenting with classical annotated datasets. The model yielded a mean accuracy of about 97.91% and an F1 Score of 0.935 in pediatric seizure detection, and mean sensitivity and specificity of 99.05% and 98.28%, in neonatal seizure detection tasks, respectively. Thus, the model can be deemed comparable to the available state-of-the-art seizure detection frameworks.
{"title":"Detecting Epileptic Seizures Using Symplectic Geometry Decomposition-Based Features and Gaussian Deep Boltzmann Machines","authors":"K. Visalini, Saravanan Alagarsamy, S. Raja","doi":"10.1142/s021946782450044x","DOIUrl":"https://doi.org/10.1142/s021946782450044x","url":null,"abstract":"Studies deem that about 1 percent of the human population is affected by epileptic seizures on a global scale. It is characterized as an undue neuronal discharge in the brain and degrades the quality of life of the patients to a large extent. Children being unaware of a sudden onset of seizures could be affected by severe injury or even mortality. Machine-learning-based epileptic seizure detection from EEG (Electro-Encephalogram) signals have always been a hot area of research. However, the majority of the research works rely on correlated non-linear features extracted from the EEG signals, causing a high-computational overhead, and challenging their application in real-time clinical diagnosis. This study proposes a robust seizure detection framework using Gaussian Deep Boltzmann Machine-based classifier and Symplectic Geometric Decomposition (SGD)-based features. The simplified eigenvalues derived through Symplectic Similarity Transform (SST) are employed as feature vectors for the classifier, eliminating the need for a deliberate feature extraction procedure. The study examines the transferability capability of the suggested framework in discriminating seizures in both neonates and pediatric subjects in unison, experimenting with classical annotated datasets. The model yielded a mean accuracy of about 97.91% and an F1 Score of 0.935 in pediatric seizure detection, and mean sensitivity and specificity of 99.05% and 98.28%, in neonatal seizure detection tasks, respectively. Thus, the model can be deemed comparable to the available state-of-the-art seizure detection frameworks.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47565113","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 : 2023-04-12DOI: 10.1142/s0219467823400090
Y. Sravani Devi, S. Phani Kumar
{"title":"A Deep Convolutional Generative Adversarial Network (DC-GAN) and Variational Auto Encoders (VAE) Models with Transfer Learning Approaches for Diabetic Retinopathy Detection","authors":"Y. Sravani Devi, S. Phani Kumar","doi":"10.1142/s0219467823400090","DOIUrl":"https://doi.org/10.1142/s0219467823400090","url":null,"abstract":"","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45124464","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 : 2023-03-31DOI: 10.1142/s0219467824500475
Dr. Nagaraj V. Dharwadkar, Ashutosh A. Lonikar, Mufti Mahmud
In this paper, we changed the methodology for pixel value differencing. The proposed method work on RGB color images improves the existing PVD technique in terms of embedding capacity and overcomes the issue of falling off boundaries in the traditional PVD technique, and provides security to the secret message from histogram quantization attack. Color images are composed of three different color channels (red, green, and blue), so we cannot apply the traditional pixel value differencing algorithm to them. Due to that, the proposed technique divides the RGB photograph in red, blue, and green channels. Following that the modified pixel value differencing algorithm is employed to all successive pixels of color channels. We get the total embedding capacity by adding the embedding capacities of each color component. After embedding the data, we concatenate the color channels to get the stegoimage. On a series of color images, we tested our pixel value differencing approach and found that the stego-picture’s visual excellence and payload capacity were reasonable. The variation in histogram between the stego and cover photographs was minor, making it resistant to histogram quantization attacks, and the suggested approach also solves the issue of falling off the boundary.
{"title":"High Embedding Capacity Color Image Steganography Scheme Using Pixel Value Differencing and Addressing the Falling-Off Boundary Problem","authors":"Dr. Nagaraj V. Dharwadkar, Ashutosh A. Lonikar, Mufti Mahmud","doi":"10.1142/s0219467824500475","DOIUrl":"https://doi.org/10.1142/s0219467824500475","url":null,"abstract":"In this paper, we changed the methodology for pixel value differencing. The proposed method work on RGB color images improves the existing PVD technique in terms of embedding capacity and overcomes the issue of falling off boundaries in the traditional PVD technique, and provides security to the secret message from histogram quantization attack. Color images are composed of three different color channels (red, green, and blue), so we cannot apply the traditional pixel value differencing algorithm to them. Due to that, the proposed technique divides the RGB photograph in red, blue, and green channels. Following that the modified pixel value differencing algorithm is employed to all successive pixels of color channels. We get the total embedding capacity by adding the embedding capacities of each color component. After embedding the data, we concatenate the color channels to get the stegoimage. On a series of color images, we tested our pixel value differencing approach and found that the stego-picture’s visual excellence and payload capacity were reasonable. The variation in histogram between the stego and cover photographs was minor, making it resistant to histogram quantization attacks, and the suggested approach also solves the issue of falling off the boundary.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44045868","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 : 2023-03-31DOI: 10.1142/s0219467824500517
Kapil Mundada, J. Kulkarni
In the medical image analysis field, brain tumors (BTs) classification is a complicated process. For effortlessly detecting the tumor devoid of any surgical interference, the radiologists are aided with automated along with computerized technology. Currently, in the field of medical image processing along with analysis, admirable progress has been made by deep learning (DL) methodologies. In medical fields, for resolving several issues, huge attention was paid to DL techniques. For automation of several performed by radiologists like (1) lesion detection, (2) segmentation, (3) classification, (4) monitoring, along with (5) also prediction of treatment response that is not achievable without software, DL might be wielded. Nevertheless, classifying BTs by utilizing magnetic resonance imaging (MRI) has various complications like the difficulty of brain structure along with the intertwining of tissues in it; additionally, the brain’s higher density nature also makes the BT Classification (BTC) process quite complex. Therefore, by utilizing novel systems, MRI-centric Automatic segmentation together with classifications of BT and swelling have been proposed to overcome the aforementioned issues. The proposed methodology underwent various operations to detect BTs effectively. Initially, by utilizing the Range-centric Otsu’s Thresholding (ROT) algorithm, the skull stripping (SS) is conducted. After that, by performing contrast enhancement (CE) along with noise removal, the skull-stripped images are pre-processed. Next, by employing the Rectilinear Watershed Segmentation (RWS) algorithm, the tumor or swelling areas are segmented. Afterward, to obtain the precise tumor or swelling region, the morphological operations are executed on the segmented areas; subsequently, the desired along with relevant features are extracted. Lastly, the features being extracted are inputted to the classifier termed Uniform Convolution neural network (UCNN). The tumor tissues along with the swelling tissues are classified precisely in the classification phase. Here, the openly accessible BT Image Segmentation Benchmark (BRATS) datasets are utilized. Then, the outcomes obtained are analogized with prevailing methodologies. The experiential outcomes displayed that the BTC is performed by the proposed model with a higher accuracy rate; thus, outshined the other prevailing models.
{"title":"MRI Image-Based Automatic Segmentation and Classification of Brain Tumor and Swelling Using Novel Methodologies","authors":"Kapil Mundada, J. Kulkarni","doi":"10.1142/s0219467824500517","DOIUrl":"https://doi.org/10.1142/s0219467824500517","url":null,"abstract":"In the medical image analysis field, brain tumors (BTs) classification is a complicated process. For effortlessly detecting the tumor devoid of any surgical interference, the radiologists are aided with automated along with computerized technology. Currently, in the field of medical image processing along with analysis, admirable progress has been made by deep learning (DL) methodologies. In medical fields, for resolving several issues, huge attention was paid to DL techniques. For automation of several performed by radiologists like (1) lesion detection, (2) segmentation, (3) classification, (4) monitoring, along with (5) also prediction of treatment response that is not achievable without software, DL might be wielded. Nevertheless, classifying BTs by utilizing magnetic resonance imaging (MRI) has various complications like the difficulty of brain structure along with the intertwining of tissues in it; additionally, the brain’s higher density nature also makes the BT Classification (BTC) process quite complex. Therefore, by utilizing novel systems, MRI-centric Automatic segmentation together with classifications of BT and swelling have been proposed to overcome the aforementioned issues. The proposed methodology underwent various operations to detect BTs effectively. Initially, by utilizing the Range-centric Otsu’s Thresholding (ROT) algorithm, the skull stripping (SS) is conducted. After that, by performing contrast enhancement (CE) along with noise removal, the skull-stripped images are pre-processed. Next, by employing the Rectilinear Watershed Segmentation (RWS) algorithm, the tumor or swelling areas are segmented. Afterward, to obtain the precise tumor or swelling region, the morphological operations are executed on the segmented areas; subsequently, the desired along with relevant features are extracted. Lastly, the features being extracted are inputted to the classifier termed Uniform Convolution neural network (UCNN). The tumor tissues along with the swelling tissues are classified precisely in the classification phase. Here, the openly accessible BT Image Segmentation Benchmark (BRATS) datasets are utilized. Then, the outcomes obtained are analogized with prevailing methodologies. The experiential outcomes displayed that the BTC is performed by the proposed model with a higher accuracy rate; thus, outshined the other prevailing models.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49530317","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 : 2023-03-31DOI: 10.1142/s0219467824500463
Girish Kulkarni, C. Manike
Diabetes is a terrible health situation characterized by high-rise blood glucose levels. If it is not predicted at an early stage, then it generates the problems in the human body like kidney failure or premature death, and stroke. Controlling blood glucose levels provides patients with helpful dietary recommendations, which are critical components of diabetes management. In the past decades, diverse conventional approaches have been executed to predict the beginning stages of diabetes mellitus depending on physical and substance tests. Still, developing a new framework that can effectively diagnose diabetes mellitus-affected patients is required. To this end, the major target of this task is to predict diabetes mellitus with an advanced accuracy rate with the help of the Heuristic-based Ensemble Model Selection Strategy (H-EMSS). In the data collection phase, the Pima Indian Diabetes dataset (PID) is taken from the storage area of UCI. The data cleaning is performed in the pre-processing stage, which is the technique of removing or fixing, corrupted, incorrect, duplicate, incomplete data, or incorrectly formatted, inside a dataset. Then, the diabetes prediction is accomplished by the H-EMSS. Here, 10 base learners like Naive Bayes (NB), Convolutional Neural Network (CNN), Linear Regression (LR), Deep Neural Network (DNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Auto Encoder (AE) and Recurrent Neural Network (RNN) are considered. From these, three classifiers are optimally selected by the Modified Scalar Factor-based Elephant Herding Optimization (MSF-EHO), so that the prediction rate will be high. The suggested methodology’s efficacy is also compared and analyzed, with the findings demonstrating the recommended model’s superiority. The overall evaluation is that the Root Mean Square Error (RMSE) of the designed Modified Scalar Factor-based Elephant Herding Optimization-Heuristic-based Ensemble Model Selection Strategy (MSF-EHO-H-EMSS) attains 4.601% and also the Mean Absolute Error (MAE) on the designed method achieves 0.99%. Thus, the given outcomes of the designed method revealed that it achieves elevated performance than the other existing techniques regarding diverse error metrics.
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