Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer

Devvi Sarwinda, Radifa Hilya Paradisa, Alhadi Bustamam, Pinkie Anggia

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper investigates a deep learning method in image classification for the detection of colorectal cancer with ResNet architecture. The exceptional performance of a deep learning classification incites scholars to implement them in medical images. In this study, we trained ResNet-18 and ResNet-50 on colon glands images. The models trained to distinguish colorectal cancer into benign and malignant. We assessed our prototypes on three varieties of testing data (20%, 25%, and 40% of whole datasets). The empirical outcomes confirm that the application of ResNet-50 provides the most reliable performance for accuracy, sensitivity, and specificity value than ResNet-18 in three kinds of testing data. Upon three test assortments, we perceive the best performance value on 20% and 25% test sets with a classification accuracy of above 80%, the sensitivity of above 87%, and the specificity of above 83%. In this research, a deep learning method demonstrates the profoundly reliable and reproducible outcomes for biomedical image analysis.

Original languageEnglish
Pages (from-to)423-431
Number of pages9
JournalProcedia Computer Science
Volume179
DOIs
Publication statusPublished - 2021
Event5th International Conference on Computer Science and Computational Intelligence, ICCSCI 2020 - Virtual, Online, Indonesia
Duration: 19 Nov 202020 Nov 2020

Keywords

  • colon glands
  • deep learning
  • image classification
  • ResNet

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