ECgMLP : A novel gated MLP model for enhanced endometrial cancer diagnosis

Journal article

Sheakh, Md. Alif, Azam, Sami, Tahosin, Mst. Sazia, Karim, Asif, Montaha, Sidratul, Fahim, Kayes Uddin, Shafiabady, Niusha, Jonkman, Mirjam and De Boer, Friso. (2025). ECgMLP : A novel gated MLP model for enhanced endometrial cancer diagnosis. Computer Methods and Programs in Biomedicine Update. 7, p. Article 100181. https://doi.org/10.1016/j.cmpbup.2025.100181
AuthorsSheakh, Md. Alif, Azam, Sami, Tahosin, Mst. Sazia, Karim, Asif, Montaha, Sidratul, Fahim, Kayes Uddin, Shafiabady, Niusha, Jonkman, Mirjam and 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.

Keywordsendometrial cancer; histopathological; gMLP; watershed; deep learning
Year2025
JournalComputer Methods and Programs in Biomedicine Update
Journal citation7, p. Article 100181
PublisherElsevier B.V.
ISSN2666-9900
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpbup.2025.100181
Scopus EID2-s2.0-85216128264
Open accessPublished as ‘gold’ (paid) open access
Page range1-18
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License
File Access Level
Open
Output statusPublished
Publication dates
Online27 Jan 2025
Publication process dates
Deposited28 May 2025
Additional information

© 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/).

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