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ECgMLP : A novel gated MLP model for enhanced endometrial cancer diagnosis
Sheakh, Md. Alif Azam, Sami Tahosin, Mst. Sazia Karim, Asif Montaha, Sidratul Fahim, Kayes Uddin Shafiabady, Niusha Jonkman, Mirjam De Boer, Friso
Sheakh, Md. Alif
Azam, Sami
Tahosin, Mst. Sazia
Karim, Asif
Montaha, Sidratul
Fahim, Kayes Uddin
Shafiabady, Niusha
Jonkman, Mirjam
De Boer, Friso
Abstract
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.
Keywords
endometrial cancer, histopathological, gMLP, watershed, deep learning
Date
2025
Type
Journal article
Journal
Computer Methods and Programs in Biomedicine Update
Book
Volume
7
Issue
Page Range
1-18
Article Number
Article 100181
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY-NC 4.0
File Access
Open
Notes
© 2025 The Authors. Published by Elsevier B.V. CCBYLICENSE This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).