Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer
Devvi Sarwinda, Radifa Hilya Paradisa, Alhadi Bustamam, Pinkie Anggia
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.