Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Abstract

Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP system uses MRI radio imaging for predicting colorectal cancer. During this process, high- and low-level features are required to examine cancer in an earlier stage. Due to the limitation of the conventional feature extraction process, both features are difficult to extract from cancer suffered locations. Hence, a deep learning system (DLS) is used to examine the entire bowel MRI image to identify the cancer-affected location, feature extraction, and feature training process. Furthermore, the DLS-based IAECP system helps improve the overall colorectal cancer identification accuracy for further process. The derived bowel features are trained by applying the residual convolution network, which minimizes the error between predicted and actual values. Finally, the test query images are compared with the trained image by applying the sum, which is more absolute to the cross-correlation template feature matching (SACC) algorithm. The experimental process is performed using 100,000 histological data sets, which is considered a publicly available data set. Moreover, the introduced method does not use generic features, whereas the deep learning features help improve the overall IAECP prediction rate (99.8%) ratio as predicted at lab-scale analysis.