CT Image Precise Denoising Model with Edge Based Segmentation with Labeled Pixel Extraction Using CNN Based Feature Extraction for Oral Cancer Detection

Oral cancer, the most prevalent form of head and neck cancer, calls for early detection to ensure better patient outcomes, reducing morbidity and mortality rates. This study explores
the application of computer vision and deep learning methods for photographic images in the oral cancer domain, investigating a two-stage pipeline for an automated system to identify oral potentially malignant abnormalities. Oral cancer staging, crucial for determining appropriate treatment and medication, often faces challenges due to noise levels in images that impact disease prediction accuracy. This research works with an image
dataset, enhancing image quality and performing denoising to improve accuracy levels. The study aims to evaluate the accuracy of an image enhancement and denoising model, resulting in quality images for extracting features for oral cancer detection. By segmenting the image using multiscale morphology methods, cell features can be extracted. The morphological edge detection method enables more precise extraction of target, cell area, perimeter, and other multi-dimensional features, followed by classification through Convolution Neural Networks (CNN). This research proposes a Precise Denoising Model with Edge-Based Segmentation for Labeled Pixel Extraction with Fixed Feature Set (PDM-ES-LPE-FFS) for relevant feature extraction. When compared with traditional models, the proposed model demonstrates superior performance