Innovative Image Processing Methods for Colorectal Tumor Identification

Abstract

Colorectal cancer is an important health issue worldwide, demanding accurate and fast diagnostic measures to reduce its impact. Traditional image processing algorithms frequently fail to effectively diagnose malignancies, resulting in incorrect diagnoses and delayed treatment. As a result, there is a critical requirement for breakthrough technologies that improve the accuracy and efficiency of colorectal tumor identification. Developed an innovative system that uses cutting-edge image processing techniques, including deep learning-based segmentation and feature extraction algorithms, to achieve more accurate and efficient tumour identification. The proposed system overcomes the limitations of existing systems by greatly increasing tumour detection accuracy, decreasing false positives and negatives, and expediting the diagnostic procedure. The proposed system consists of numerous essential parts, including data collecting, image processing, segmentation, feature extraction, classification, and model evaluation. Accurate evaluation and validation demonstrate that the proposed system consistently surpasses existing systems in terms of segmentation accuracy (93.65%), sensitivity (89.92%), specificity (94.10%), and AUC-ROC (95.63%). Furthermore, the proposed system has excellent image processing performance, including noise reduction (0.87), contrast enhancement (0.91), and image normalisation (0.93), which improves the quality and usability of colorectal tissue pictures for accurate tumour diagnosis.