Classification and segmentation of kidney MRI images for chronic kidney disease detection
<p>Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513552619929600 |
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| author | Md. Sakib Bin Islam (20494559) |
| author2 | Md. Shaheenur Islam Sumon (17983810) Rusab Sarmun (17632269) Enamul H. Bhuiyan (17983816) Muhammad E.H. Chowdhury (17151154) |
| author2_role | author author author author |
| author_facet | Md. Sakib Bin Islam (20494559) Md. Shaheenur Islam Sumon (17983810) Rusab Sarmun (17632269) Enamul H. Bhuiyan (17983816) Muhammad E.H. Chowdhury (17151154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Sakib Bin Islam (20494559) Md. Shaheenur Islam Sumon (17983810) Rusab Sarmun (17632269) Enamul H. Bhuiyan (17983816) Muhammad E.H. Chowdhury (17151154) |
| dc.date.none.fl_str_mv | 2024-08-31T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compeleceng.2024.109613 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Classification_and_segmentation_of_kidney_MRI_images_for_chronic_kidney_disease_detection/28123448 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Chronic kidney disease (CKD) Machine learning Diagnosis Classification |
| dc.title.none.fl_str_mv | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.</p><h2>Other Information</h2> <p> Published in: Computers and Electrical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109613" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109613</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4fe5c33968cf29eab509191415f476f3 |
| identifier_str_mv | 10.1016/j.compeleceng.2024.109613 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28123448 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Classification and segmentation of kidney MRI images for chronic kidney disease detectionMd. Sakib Bin Islam (20494559)Md. Shaheenur Islam Sumon (17983810)Rusab Sarmun (17632269)Enamul H. Bhuiyan (17983816)Muhammad E.H. Chowdhury (17151154)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceChronic kidney disease (CKD)Machine learningDiagnosisClassification<p>Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.</p><h2>Other Information</h2> <p> Published in: Computers and Electrical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109613" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109613</a></p>2024-08-31T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compeleceng.2024.109613https://figshare.com/articles/journal_contribution/Classification_and_segmentation_of_kidney_MRI_images_for_chronic_kidney_disease_detection/28123448CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281234482024-08-31T12:00:00Z |
| spellingShingle | Classification and segmentation of kidney MRI images for chronic kidney disease detection Md. Sakib Bin Islam (20494559) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Chronic kidney disease (CKD) Machine learning Diagnosis Classification |
| status_str | publishedVersion |
| title | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| title_full | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| title_fullStr | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| title_full_unstemmed | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| title_short | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| title_sort | Classification and segmentation of kidney MRI images for chronic kidney disease detection |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Chronic kidney disease (CKD) Machine learning Diagnosis Classification |