Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems. Due to the advancements of medical imaging in healthcare decision making, significant attention has been paid by the computer vision and deep learning (DL) models. At the same time, the detection and classification of colorectal cancer (CC) become essential to reduce the severity of the disease at an earlier stage. The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning (ML) to recognize the existence of diseases. In this aspect, this study focuses on the design of intelligent DL based CC detection and classification (IDL-CCDC) model for bioinformatics applications. The proposed IDL-CCDC technique aims to detect and classify different classes of CC. In addition, the IDL-CCDC technique involves fuzzy filtering technique for noise removal process. Moreover, water wave optimization (WWO) based EfficientNet model is employed for feature extraction process. Furthermore, chaotic glowworm swarm optimization (CGSO) based variational auto encoder (VAE) is applied for the classification of CC into benign or malignant. The design of WWO and CGSO algorithms helps to increase the overall classification accuracy. The performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.