A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer

Ziynet Pamuk; Hüseyin Erikçi

doi:10.35377/saucis...1638424

EN

A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer

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.

Keywords

Ethical Statement

This study was conducted using data obtained from the Kaggle page and does not require ethical permission.

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Ziynet Pamuk ^*
0000-0003-3792-2183
Türkiye

Hüseyin Erikçi
0000-0003-3988-9823
Türkiye

Early Pub Date

March 27, 2025

Publication Date

March 28, 2025

Submission Date

February 12, 2025

Acceptance Date

March 23, 2025

Published in Issue

Year 2025 Volume: 8 Number: 1

DOI

https://doi.org/10.35377/saucis...1638424

IZ

https://izlik.org/JA57XX58LG

Cite

RIS / Bibtex

APA

Pamuk, Z., & Erikçi, H. (2025). A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. Sakarya University Journal of Computer and Information Sciences, 8(1), 136-151. https://doi.org/10.35377/saucis...1638424

AMA

1.Pamuk Z, Erikçi H. A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. SAUCIS. 2025;8(1):136-151. doi:10.35377/saucis.1638424

Chicago

Pamuk, Ziynet, and Hüseyin Erikçi. 2025. “A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer”. Sakarya University Journal of Computer and Information Sciences 8 (1): 136-51. https://doi.org/10.35377/saucis. 1638424.

EndNote

Pamuk Z, Erikçi H (March 1, 2025) A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. Sakarya University Journal of Computer and Information Sciences 8 1 136–151.

IEEE

[1]Z. Pamuk and H. Erikçi, “A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer”, SAUCIS, vol. 8, no. 1, pp. 136–151, Mar. 2025, doi: 10.35377/saucis...1638424.

ISNAD

Pamuk, Ziynet - Erikçi, Hüseyin. “A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 1, 2025): 136-151. https://doi.org/10.35377/saucis. 1638424.

JAMA

1.Pamuk Z, Erikçi H. A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. SAUCIS. 2025;8:136–151.

MLA

Pamuk, Ziynet, and Hüseyin Erikçi. “A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, Mar. 2025, pp. 136-51, doi:10.35377/saucis. 1638424.

Vancouver

1.Ziynet Pamuk, Hüseyin Erikçi. A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. SAUCIS. 2025 Mar. 1;8(1):136-51. doi:10.35377/saucis. 1638424

Öz

A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer

Abstract

Keywords

Ethical Statement

References

Details

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Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite