Novel approach in detecting colon cancer using dcnn: A systematic study

Colon cancer remains a significant global health concern, and early detection is crucial for effective treatment and improved patient outcomes. Histopathological examination of tissue samples is a standard diagnostic procedure, but the manual analysis of large datasets is time-consuming and prone to human error. This study proposes a novel approach for colon cancer detection by Deep Convolutional Neural Networks (DCNN). This research focuses on developing an automated system that utilizes DCNN to analyze histopathological images of colon tissues. The DCNN model is trained on a comprehensive dataset (LC25000) comprising both adenocarcinoma and benign tissue samples. The model's ability to extract high-level representations from complex image data allows for accurate and efficient classification. The evaluation of the proposed system involves assessing its performance in terms of sensitivity, specificity, and overall accuracy. In this DCNN, the model get 98.7% accuracy, 99% precision, 99% recall, 99% F1 score. The results demonstrate the model's capability to accurately identify cancerous regions within histopathological images, providing a valuable tool for pathologists and clinicians.