Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm with Deep Learning Model

Abstract

The domain of Artificial Intelligence (AI) is made important strides recently, leading to developments in several domains comprising biomedical diagnostics and research. The procedure of AI-based systems in biomedical analytics takes opened up novel avenues for the progress of disease analysis, drug discovery, and treatment. Cancer is the second major reason of death worldwide; around one in every six people pass away suffering from it. Among several kinds of cancers, the colon and lung variations are the most frequent and deadliest ones. Initial detection of conditions on both fronts significantly reduces the probability of mortality. Deep learning (DL) and Machine learning (ML) systems are exploited to speed up such cancer detection, permitting researchers to analyze a huge count of patients in a lesser time count and at a minimal cost. This study develops a new Biomedical Image Analysis for Colon and Lung Cancer Detection using Tuna Swarm Algorithm with Deep Learning (BICLCD-TSADL) model. The presented BICLCD-TSADL technique examines the biomedical images for the identification and classification of colon and lung cancer. To accomplish this, the BICLCD-TSADL technique applies Gabor filtering (GF) to preprocess the input images. In addition, the BICLCD-TSADL technique employs a GhostNet feature extractor to create a collection of feature vectors. Moreover, AFAO was executed to adjust the hyperparameters of the GhostNet technique. Furthermore, the TSA with echo state network (ESN) classifier is utilized for detecting lung and colon cancer. To demonstrate the more incredible outcome of the BICLCD-TSADL system, an extensive experimental outcome is carried out. The comprehensive comparative analysis highlighted the greater efficiency of the BICLCD-TSADL technique with other approaches with maximum accuracy of 99.33%.