Enhancing Colorectal Cancer Automatic Diagnosis using Artificial Intelligence Techniques Depending on Preferable Medical Scan

Abstract

One of the most common causes of death is colorectal cancer (CRC). The spread of cancer cells to other organs increases dramatically because of delayed detection. Presently, the only ways to increase survival rates and reduce cancerrelated mortality are via prompt diagnosis and customized therapies. Artificial intelligence (AI) may significantly aid professionals in identifying CRC cases with less effort, time, and cost. This study presents a novel convolutional neural network (CNN) for detection known as COCDNet and two sets of modifications to CNN models for identifying cecum CRC in computed tomography (CT) radiological scans. Before images are included in the architecture, they are preprocessed to reduce the noise. The data is then sent into a COCDNet model that holds 22 layers. On other hand, two types of transfer learning (TL) are used in four popular CNN models: DarkNet19, VGG16, VGG19, and AlexNet. The dataset comprises 1,695 images of abdomen CT scans, categorized into two main classes as cecum cancer and normal images. COCDNet achieves the highest performance, proving an accuracy of 97.04%, an F1-score of 95.80%, and recall approaching 100%. These measures demonstrate that COCDNet is a dependable tool for early CRC diagnosis because it can both reliably detect cancer and reduce false positives. The suggested model success in detecting cecum CRC demonstrates the value of this work that improves AI models for bettering healthcare systems and saving lives.