Automatic lung and colon cancer detection using enhanced cascade convolution neural network

Several types of cancer can develop in different organs at the same time, and lung and colon cancer can affect the human body adversely in some cases. To diagnose cancer cases, experts must go through a lengthy and complicated process of analyzing histopathological images. Still, the process can be quickly achieved with the technological possibilities currently available. However, lung and colon cancer (LCC) classification requires improvement. For this reason, we developed a method for classifying LCC using a deep learning technique. In the proposed methodology, there are three phases, namely pre-processing, segmentation, and classification. As a first step, histopathological images are collected and preprocessed. It is necessary to use a median filter for pre-processing. The segmentation process is then conducted on the pre-processed images. The swin transformer is used to segment the data. The lung nodules and colon are segmented as a result of the segmentation process. To classify the image as normal or cancerous, these segmented parts are fed into the enhanced cascade convolution neural network classifier (EC2N2). To improve the efficiency of the cascade classifier, the hyper-parameters are optimally selected using the Adaptive Tasmanian Devil Optimization (ATDO) algorithm. The efficiency of the presentedtechnique is analysed based on various metrics and research work compared with different state-of-art-works.