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

Yıl 2025, , 136 - 151, 28.03.2025

Ziynet Pamuk , Hüseyin Erikçi

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

Öz

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.

Anahtar Kelimeler

Microsatellite instability Deep learning Colorectal cancer Histopathologic image

Etik Beyan

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

Kaynakça

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Yıl 2025, , 136 - 151, 28.03.2025

Ziynet Pamuk , Hüseyin Erikçi

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

Öz

Kaynakça

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  • S. J. Choi, E. S. Kim, and K. Choi, “Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms,” Scientific Reports, vol. 11, no. 1, p. 5311, 2021, doi: 10.1038/s41598-021-84299-2.
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Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Research Article
Yazarlar

Ziynet Pamuk 0000-0003-3792-2183

Hüseyin Erikçi 0000-0003-3988-9823

Erken Görünüm Tarihi 27 Mart 2025
Yayımlanma Tarihi 28 Mart 2025
Gönderilme Tarihi 12 Şubat 2025
Kabul Tarihi 23 Mart 2025
Yayımlandığı Sayı Yıl 2025

Kaynak Göster

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 Pamuk Z, Erikçi H. A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. SAUCIS. Mart 2025;8(1):136-151. doi:10.35377/saucis.1638424
Chicago Pamuk, Ziynet, ve 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 8, sy. 1 (Mart 2025): 136-51. https://doi.org/10.35377/saucis. 1638424.
EndNote Pamuk Z, Erikçi H (01 Mart 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 Z. Pamuk ve H. Erikçi, “A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer”, SAUCIS, c. 8, sy. 1, ss. 136–151, 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 (Mart 2025), 136-151. https://doi.org/10.35377/saucis. 1638424.
JAMA 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 ve 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, c. 8, sy. 1, 2025, ss. 136-51, doi:10.35377/saucis. 1638424.
Vancouver Pamuk Z, Erikçi H. A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer. SAUCIS. 2025;8(1):136-51.

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