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.
Highlights
Worldwide, colorectal cancer [1] is considered one of the common cancers
After removing the polyp from the colon, most of the time, dysplasia is formed based on the precancerous condition, whereas it is more unique than the cancer cell. is colorectal cancer is categorized into several types [5], such as adenocarcinoma, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor, primary colorectal lymphoma, and sarcoma. is colon or colorectal cancer has several symptoms [6], such as diarrhea, bleeding in stool, fatigue, unexpected weight loss, and abdominal discomfort
According to the same reasoning, the network should anticipate which function it had previously learned by additional input when we provide the input to the first level of the model to be the output of the last layer of the model
Summary
Colorectal cancer [1] is considered one of the common cancers. In 2012, around 1,360,000 cases were suffered due to colorectal cancer, among 447,000 patients in Europe [2]. is cancer is the second most common cancer; due to this rectal cancer, 125,000 people have died. e environmental influence, hereditary changes, and genetic mutation accumulations were the main rectal cancer risk factors [3]. The main risk factors [7] are inherited syndromes, family history, sedentary lifestyle, unhealthy diet, obesity, and smoking Due to these risk factors, colorectal cancers occur in people. E automatic detection system uses various machine learning and image analysis techniques [10] to predict the changes in their colon. Several methods such as support vector machine, neural networks, convolution networks, extreme learning approach, feedforward network, region segmentation, edge analysis, and dual clustering approaches are used to examine the colorectal MRI image. The DLS-based image analysis and earlier cancer prediction (IAECP) system help enhance the total colorectal cancer recognition accuracy for further processes. 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
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