Accurate Detection and Prognostic Assessment of Colon Cancer Using an R-MobileNet-Driven Deep Learning Model with Clinical Structured Data

Abstract

Proper diagnosis and prognosis of colon cancer is essential to enhancing patient outcomes and informing individual treatment plans. This paper has formulated a deep learning model that is powered by the R-MobileNet design to perform effective colon cancer classification through clinical structured data. The model relied on a variety of data, featuring systematic pre-processing measures and careful validation to achieve as much predictive reliability as possible. Measures of evaluation related to ROC curve analysis, classification accuracy, predicted class distribution, and confusion matrix were evaluated, and the R-MobileNet model consistently performed well on validation datasets, with an area under the curve (AUC) of 0.75 and a high classification accuracy of 0.96. Predicted classes distribution and confusion matrix indicate successful discrimination of the relevant cell types and highlight the ability of this method to accurately detect and stratify prognosis. These findings confirm the incorporation of advanced deep learning algorithms such as R-MobileNet into the clinical decision-making process in managing colon cancer.