@article{article_1638424, title={A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer}, journal={Sakarya University Journal of Computer and Information Sciences}, volume={8}, pages={136–151}, year={2025}, DOI={10.35377/saucis...1638424}, url={https://izlik.org/JA57XX58LG}, author={Pamuk, Ziynet and Erikçi, Hüseyin}, keywords={Microsatellite instability, Deep learning, Colorectal cancer, Histopathologic image}, abstract={Colorectal cancer remains one of the most prevalent and fatal malignancies worldwide, underscoring the necessity for early and precise diagnostic approaches to enhance patient prognoses. This study proposes a deep learning-based model for predicting microsatellite instability (MSI) in colorectal cancer using hematoxylin and eosin (H&E)-stained histopathological tissue slides. A classification framework was constructed using convolutional neural networks (CNN) and optimized through transfer learning techniques. The dataset, comprising 150,000 unique H&E-stained histologic image patches, was sourced from an open-access Kaggle repository, with 80% allocated to training and 20% to testing. A comparative evaluation of nine pre-trained models demonstrated that the VGG19 architecture yielded the highest classification performance, achieving an accuracy of 90.60%, a precision of 88.60%, a sensitivity of 93.10%, and an AUC score of 90.60%. Considering its high performance, the proposed model is expected to assist pathologists in clinical decision-making, potentially enhancing diagnostic accuracy in real-world medical applications.}, number={1}