Optimizing Deep Learning Algorithms for Colorectal Tumor Identification via Noise Removal

Colorectal cancer is one of the leading causes of cancer death globally. Deep learning algorithms have demonstrated promising results in automating the detection of colorectal tumors with the increased availability of medical imaging data. However, noise in medical imaging data can make solid diagnosis of microscopic tumors difficult. In the proposed study, offered a unique method for enhancing deep learning algorithms for colorectal tumor detection by removing noise. Combine a denoisingautoencoder (DAE) with a convolutional neural network (CNN) to increase the reliability of the tumor identification process. The denoisingautoencoder (DAE) successfully reduces noise from the input images, allowing the neural network that follows to focus on the key elements for accurate tumor diagnosis. Experiment findings on a The Cancer Imaging Archive (TCIA) dataset show that the proposed approach is efficient, with a considerable reduction in false positives and false negatives when compared to existing methodologies. The technique helps to enhance the early and accurate identification of colorectal tumors, potentially contributing to more immediate and efficient patient treatments.