MULTI-LEVEL IMAGE DENOISING INTEGRATED MORPHOLOGY-BASED IMAGE QUALITY ENHANCEMENT MODEL WITH EDGE-BASED SEGMENTATION FOR ACCURATE PANCREATIC CANCER DETECTION

Despite the availability of various methods, diagnosing and treating Pancreatic Cancer (PC) continues as a significant challenge across all types of tumours with its asymptomatic development. Among the most devastating diseases, pancreatic cancer has claimed the lives of countless people around the globe. Traditional methods of diagnosis relied on manual analysis of massive datasets, which was laborious, error-prone, and time-consuming. Therefore, CADs, which use machine learning and deep learning techniques for pancreatic cancer denoising, segmentation, and classification, become necessary. However, there are obstacles to medical image analysis of pancreatic cancer owing to vague symptoms, high rates of misdiagnosis, and substantial monetary expenses. A potential answer is Artificial Intelligence (AI), which can reduce patient expenditures, improve clinical decision-making, and alleviate the workload of medical workers. The use of medical imaging scans has allowed many cancer patients to detect anomalies earlier on. This research makes use of Computed Tomography (CT) images for performing image denoising and segmentation. The high price of the required equipment and infrastructure makes it difficult to spread the technology, which means that many people cannot afford it. Pancreatic cancer detection using medical image analysis is greatly impeded by noisy, low-quality images that mask important diagnostic details and lower detection accuracy. This research tackles the important problem of creating a state-of-the-art image processing method that can reliably and effectively denoise medical images, improve image quality using morphological techniques, and apply precise edge-based segmentation to better visualize and detect pancreatic cancer early on. In this research, a new Multi-Level Image Denoising and Integrated Morphology based Image Quality Enhancement Model (MIDIMIQEM) by edge-based segmentation for precise pancreatic cancer detection is proposed. This proposed model solve these problems in medical image analysis by introducing multi level denoising technique and also morphology-based enhancement with a robust edge based segmentation for an enhanced diagnostic accuracy. The model uses wavelet based multilevel image denoising for the removal of noise and thereafter morphological operations are employed to improve the contrast which helps in easier differentiable identification of tumor structures. The segmented regions are further improved by the edge detection techniques, which increases the chances of self and accurate determination of cancerous tissues. Experimental evaluation on MIDIMIQEM demonstrates that the image quality, segmentation accuracy and small nodules detection performance are significantly improved compared with state-of-the-art models. The proposed model achieved 98.7% accuracy in Multi-Level Image Denoising and 99.3% accuracy in Segmentation Accuracy. This novel technique is expected to help radiologists make an early and accurate diagnosis that could improve patient outcomes. With the use of cutting-edge edge-based morphological processing methods, the suggested multi-level picture denoising and morphology-based enhancement model outperforms state-of-the-art methods for pancreatic cancer image segmentation, greatly enhancing diagnostic precision and opening up new avenues for early detection.