ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis
Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are emplo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990025000059 |
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| author | Md. Alif Sheakh Sami Azam Mst. Sazia Tahosin Asif Karim Sidratul Montaha Kayes Uddin Fahim Niusha Shafiabady Mirjam Jonkman Friso De Boer |
| author_facet | Md. Alif Sheakh Sami Azam Mst. Sazia Tahosin Asif Karim Sidratul Montaha Kayes Uddin Fahim Niusha Shafiabady Mirjam Jonkman Friso De Boer |
| author_sort | Md. Alif Sheakh |
| collection | DOAJ |
| description | Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes. |
| format | Article |
| id | doaj-art-2dfb7c81167348fcb325e208c1625d82 |
| institution | Kabale University |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-2dfb7c81167348fcb325e208c1625d822025-01-28T04:14:57ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-017100181ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosisMd. Alif Sheakh0Sami Azam1Mst. Sazia Tahosin2Asif Karim3Sidratul Montaha4Kayes Uddin Fahim5Niusha Shafiabady6Mirjam Jonkman7Friso De Boer8Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1341, BangladeshFaculty of Science and Technology, Charles Darwin University, Darwin, NT 0909, AustraliaHealth Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1341, BangladeshFaculty of Science and Technology, Charles Darwin University, Darwin, NT 0909, Australia; Corresponding author.Department of Computer Science and Engineering, University of Calgary, Calgary, CanadaHealth Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1341, BangladeshDepartment of Information Technology, Australian Catholic University, North Sydney 2060, NSW, AustraliaFaculty of Science and Technology, Charles Darwin University, Darwin, NT 0909, AustraliaFaculty of Science and Technology, Charles Darwin University, Darwin, NT 0909, AustraliaEndometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes.http://www.sciencedirect.com/science/article/pii/S2666990025000059Endometrial cancerHistopathologicalgMLPWatershedDeep learning |
| spellingShingle | Md. Alif Sheakh Sami Azam Mst. Sazia Tahosin Asif Karim Sidratul Montaha Kayes Uddin Fahim Niusha Shafiabady Mirjam Jonkman Friso De Boer ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis Computer Methods and Programs in Biomedicine Update Endometrial cancer Histopathological gMLP Watershed Deep learning |
| title | ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis |
| title_full | ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis |
| title_fullStr | ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis |
| title_full_unstemmed | ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis |
| title_short | ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis |
| title_sort | ecgmlp a novel gated mlp model for enhanced endometrial cancer diagnosis |
| topic | Endometrial cancer Histopathological gMLP Watershed Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000059 |
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