TY  - JOUR
T1  - A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer
AU  - Pamuk, Ziynet
AU  - Erikçi, Hüseyin
PY  - 2025
DA  - March
Y2  - 2025
DO  - 10.35377/saucis...1638424
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 136
EP  - 151
VL  - 8
IS  - 1
LA  - en
AB  - 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.
KW  - Microsatellite instability
KW  - Deep learning
KW  - Colorectal cancer
KW  - Histopathologic image
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UR  - https://doi.org/10.35377/saucis...1638424
L1  - https://dergipark.org.tr/en/download/article-file/4603040
ER  -