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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Fatiha El Ouaai
University Abdelmalek Essaadi
Computer Science & IT Engineering

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Deep learning for categorizing microsatellite stability in colorectal cancer Sofyan El Idrissi; Yassine Drider; Ikram Ben Abdel Ouahab; Mohammed Bouhorma; Fatiha El Ouaai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3761-3769

Abstract

Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Co-Authors Ikram Ben Abdel Ouahab Mohammed Bouhorma Sofyan El Idrissi Yassine Drider