RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis
Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/10/1/10 |
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| author | S. M. Nuruzzaman Nobel S M Masfequier Rahman Swapno Md. Ashraful Hossain Mejdl Safran Sultan Alfarhood Md. Mohsin Kabir M. F. Mridha |
| author_facet | S. M. Nuruzzaman Nobel S M Masfequier Rahman Swapno Md. Ashraful Hossain Mejdl Safran Sultan Alfarhood Md. Mohsin Kabir M. F. Mridha |
| author_sort | S. M. Nuruzzaman Nobel |
| collection | DOAJ |
| description | Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical. This study primarily uses a unique dataset painstakingly selected from 20 esteemed medical institutes. The dataset includes a wide range of images, such as tissue microarray (TMA) images at 40× magnification and whole-slide images (WSI) at 20× magnification. The research is fully committed to identifying abnormalities within this complex environment, going beyond the classification of subtypes of ovarian cancer. We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. Using images magnified WSI, the model demonstrated an astonishing 96.42% training accuracy and 95.10% validation accuracy. Similarly, with images magnified via a TMA, the model performed well, obtaining a validation accuracy of 94.90% and a training accuracy of 93.45%. Our fine-tuned hyperparameter testing resulted in exceptional performance on independent images. At 20× magnification, we achieved an accuracy of 93.56%. Even at 40× magnification, our testing accuracy remained high, at 91.37%. This study highlights how machine learning can revolutionize the medical field’s ability to classify ovarian cancer subtypes and identify outliers, giving doctors a valuable tool to lessen the severe effects of the disease. Adopting this novel method is likely to improve the practice of medicine and give people living with ovarian cancer worldwide hope. |
| format | Article |
| id | doaj-art-038cd72f49ff4c618e14dde4693c5f9b |
| institution | Kabale University |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-038cd72f49ff4c618e14dde4693c5f9b2025-02-07T15:25:04ZengMDPI AGTomography2379-13812379-139X2024-01-0110110513210.3390/tomography10010010RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer DiagnosisS. M. Nuruzzaman Nobel0S M Masfequier Rahman Swapno1Md. Ashraful Hossain2Mejdl Safran3Sultan Alfarhood4Md. Mohsin Kabir5M. F. Mridha6Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, BangladeshDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaSuperior Polytechnic School, University of Girona, 17071 Girona, SpainDepartment of Computer Science, American International University-Bangladesh, Dhaka 1229, BangladeshOvarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical. This study primarily uses a unique dataset painstakingly selected from 20 esteemed medical institutes. The dataset includes a wide range of images, such as tissue microarray (TMA) images at 40× magnification and whole-slide images (WSI) at 20× magnification. The research is fully committed to identifying abnormalities within this complex environment, going beyond the classification of subtypes of ovarian cancer. We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. Using images magnified WSI, the model demonstrated an astonishing 96.42% training accuracy and 95.10% validation accuracy. Similarly, with images magnified via a TMA, the model performed well, obtaining a validation accuracy of 94.90% and a training accuracy of 93.45%. Our fine-tuned hyperparameter testing resulted in exceptional performance on independent images. At 20× magnification, we achieved an accuracy of 93.56%. Even at 40× magnification, our testing accuracy remained high, at 91.37%. This study highlights how machine learning can revolutionize the medical field’s ability to classify ovarian cancer subtypes and identify outliers, giving doctors a valuable tool to lessen the severe effects of the disease. Adopting this novel method is likely to improve the practice of medicine and give people living with ovarian cancer worldwide hope.https://www.mdpi.com/2379-139X/10/1/10ovarian cancerattention embeddertransfer learningcancer subtypecomputer visionoutlier detection |
| spellingShingle | S. M. Nuruzzaman Nobel S M Masfequier Rahman Swapno Md. Ashraful Hossain Mejdl Safran Sultan Alfarhood Md. Mohsin Kabir M. F. Mridha RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis Tomography ovarian cancer attention embedder transfer learning cancer subtype computer vision outlier detection |
| title | RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis |
| title_full | RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis |
| title_fullStr | RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis |
| title_full_unstemmed | RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis |
| title_short | RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis |
| title_sort | retracted modern subtype classification and outlier detection using the attention embedder to transform ovarian cancer diagnosis |
| topic | ovarian cancer attention embedder transfer learning cancer subtype computer vision outlier detection |
| url | https://www.mdpi.com/2379-139X/10/1/10 |
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