An Effective Approach for Detecting Colon Cancer Using Deep Convolutional Neural Network

Abstract

One of the leading causes of sickness and mortality in the world is colon cancer, which is definitively diagnosed by histological investigation. While both conventional and current approaches may compare images that may cover cancer areas of different kinds after looking at a large number of images of colon cancer, it remains a major cause of cancer-related mortality. Diagnosing benign from malignant disorders to have several complex elements is the primary challenge for colon histopathologists. This is done by applying techniques from image processing and deep learning (DL). The utilisation of whole-slide photography and digital image processing has made it possible to analyse colon cancer using convolutional neural networks. Proficiency in analysis requires accurate classification of colon tumours. In this paper, an improved system for recognising and classifying colon adenocarcinomas by using an adaptive wiener filter to digital histopathology images and a deep convolutional neural network (DCNN) model shows 94.7% accuracy. In the following step, the image is routed to the areas for segmentation using K-means to accurately define the illness's size and shape. Grey level Concurrent Matric (GLCM) is used for feature extraction and by putting this strategy into practice, doctors can create an automated and best method for identifying different types of colon cancer. This work is implemented using MATLAB.