Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification

Abstract

The analysis of WSI images is widely acknowledged as a method, for identifying stages of cancer and evaluating the spread of cancer cells in tissues. In histopathology image analysis deep learning models are gaining increasing importance. To enhance the effectiveness of these models it is crucial to train and fine-tune Convolutional Neural Network algorithms by adjusting hyperparameters like batch size, convolution depth, and learning rate (LR). However, determining the hyperparameters can be challenging as they significantly impact model performance. This study examines how hyperparameters influence cancer classification, in histopathology images using the CNN architecture. A method called iterative randomized hyperparameter optimization is proposed, which gradually reduces variations over time by adjusting parameters after each network layer. The combination of hyperparameters is applied to version 1 of ResNet18, ResNet50, and ResNet101 models and version 2 of ResNet50, ResNet101, and ResNet151. The results are then combined using the Adaptive Boosting algorithm. The results are quite promising on ensemble version 1 residual networks, achieving an accuracy of 98.7% when tested on nine tissue classes.