Enhancing Colorectal Cancer Treatment through Automated Segmentation with Convolutional Neural Networks and Machine Learning Approaches

The present research applies advanced automated segmentation techniques to MRI data, addressing the urgent need for improved colorectal cancer therapy. An extensive dataset of 2032 images collected from multiple online sources was used to thoroughly train and assess Naive Bayes (NB), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs). CNN predicts the response with an outstanding accuracy of 98.76% and capacity to recognize intricate patterns connected to colorectal cancer histology. The SVM and NB models demonstrated good performances with a minimal latency, with accuracy of 94.5% and 89.9%, respectively, suggesting their suitability in specific medical imaging scenarios. After a thorough examination that took into account the Hausdorff distance, memory, accuracy, and F1 score, it was evident what the benefits and drawbacks of each model were. The results show a notable advancement in automated segmentation methods, which may result in more individualized treatment regimens, accurate diagnosis, and medications for colorectal cancer. The findings not only signify a significant progression in the domain of artificial intelligence in medical imaging, but they also offer prospects for additional investigation concerning the amalgamation of multi-modal imaging data and continuous model optimization. In result, our work bridges the knowledge gap between cutting-edge technologies and practical implementation by offering insightful information that can benefit both the larger medical community and patients with colorectal cancer.