Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques
Medical images are the standard approach for the analysis and diagnosis of critical issues of diseases. To minimize the time-consuming inspection and evaluation process of the medical images from physicians in diagnosis, an automatic segmentation mechanism of abnormal features in medical images is r...
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2024-01-01
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| author | Bibat Thokar Binod Sapkota Babu R. Dawadi Shashidhar R. Joshi |
| author_facet | Bibat Thokar Binod Sapkota Babu R. Dawadi Shashidhar R. Joshi |
| author_sort | Bibat Thokar |
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| description | Medical images are the standard approach for the analysis and diagnosis of critical issues of diseases. To minimize the time-consuming inspection and evaluation process of the medical images from physicians in diagnosis, an automatic segmentation mechanism of abnormal features in medical images is required. To address the limited availability of medical image data, deep learning frameworks for multi-class image segmentation have been implemented. Moreover, the existing deep learning frameworks are static. Hence to make dynamic for the image segmentation purpose, the existing deep learning-based frameworks for medical image segmentation have been updated by integrating advanced architectures to observe performance enhancements. The existing UNet model has been integrated with a vision transformer to capture the structural properties of the medical image. Similarly, the ResNet50 architecture has been integrated with DeepLabv3plus for better extraction of features. The CVC-ClinicDB dataset, ISIC dataset, Brain Tumor dataset and HyperKVasir dataset have been collected from multiple sources. The collected datasets have been processed with image augmentation, Contrast Limited Adaptive Histogram equalization (CLAHE), and normalization. The preprocessed image dataset has been categorized into training, validation, and testing parts and used accordingly. The training image data has been used to train the multiple deep-learning models. The adaptive moment (Adam) optimizer has been used for the optimization process. The loss measurement has been carried out using categorical cross-entropy. The trained models have been evaluated using a testing dataset. The performance of the implemented deep learning framework has been measured by Accuracy, Precision, Recall, F1-Score, Dice Coefficient, and Validation Dice Coefficient metrics. |
| format | Article |
| id | doaj-art-2aadb442502a44ccb9510e3c7e57f67e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-2aadb442502a44ccb9510e3c7e57f67e2024-12-14T00:01:38ZengIEEEIEEE Access2169-35362024-01-011218546018548110.1109/ACCESS.2024.351266410781402Medical Image Segmentation for Anomaly Detection Using Deep Learning TechniquesBibat Thokar0https://orcid.org/0009-0005-9205-0885Binod Sapkota1https://orcid.org/0009-0009-9418-0092Babu R. Dawadi2Shashidhar R. Joshi3Department of Electronics and Computer Engineering, Thapathali Campus, Tribhuvan University, Kathmandu, NepalDepartment of Electronics and Computer Engineering, Thapathali Campus, Tribhuvan University, Kathmandu, NepalDepartment of Electronics and Computer Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur, NepalDepartment of Electronics and Computer Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur, NepalMedical images are the standard approach for the analysis and diagnosis of critical issues of diseases. To minimize the time-consuming inspection and evaluation process of the medical images from physicians in diagnosis, an automatic segmentation mechanism of abnormal features in medical images is required. To address the limited availability of medical image data, deep learning frameworks for multi-class image segmentation have been implemented. Moreover, the existing deep learning frameworks are static. Hence to make dynamic for the image segmentation purpose, the existing deep learning-based frameworks for medical image segmentation have been updated by integrating advanced architectures to observe performance enhancements. The existing UNet model has been integrated with a vision transformer to capture the structural properties of the medical image. Similarly, the ResNet50 architecture has been integrated with DeepLabv3plus for better extraction of features. The CVC-ClinicDB dataset, ISIC dataset, Brain Tumor dataset and HyperKVasir dataset have been collected from multiple sources. The collected datasets have been processed with image augmentation, Contrast Limited Adaptive Histogram equalization (CLAHE), and normalization. The preprocessed image dataset has been categorized into training, validation, and testing parts and used accordingly. The training image data has been used to train the multiple deep-learning models. The adaptive moment (Adam) optimizer has been used for the optimization process. The loss measurement has been carried out using categorical cross-entropy. The trained models have been evaluated using a testing dataset. The performance of the implemented deep learning framework has been measured by Accuracy, Precision, Recall, F1-Score, Dice Coefficient, and Validation Dice Coefficient metrics.https://ieeexplore.ieee.org/document/10781402/DeepLabv3plusmedical imagesegmentationUNetvision transformer |
| spellingShingle | Bibat Thokar Binod Sapkota Babu R. Dawadi Shashidhar R. Joshi Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques IEEE Access DeepLabv3plus medical image segmentation UNet vision transformer |
| title | Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques |
| title_full | Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques |
| title_fullStr | Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques |
| title_full_unstemmed | Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques |
| title_short | Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques |
| title_sort | medical image segmentation for anomaly detection using deep learning techniques |
| topic | DeepLabv3plus medical image segmentation UNet vision transformer |
| url | https://ieeexplore.ieee.org/document/10781402/ |
| work_keys_str_mv | AT bibatthokar medicalimagesegmentationforanomalydetectionusingdeeplearningtechniques AT binodsapkota medicalimagesegmentationforanomalydetectionusingdeeplearningtechniques AT baburdawadi medicalimagesegmentationforanomalydetectionusingdeeplearningtechniques AT shashidharrjoshi medicalimagesegmentationforanomalydetectionusingdeeplearningtechniques |