Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data

Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.