International Journal of Science, Engineering and TechnologyAuthors: P V S R Saiharsha, Assistant Professor M.Prasanna Kumar
Abstract: The aggressive nature of pancreatic cancer and the lack of viable early detection technologies make it a significant health danger. By combining deep learning with image processing methods, this study presents a new framework for accurately detecting pancreatic tumours. As part of the suggested system, CT images are enhanced using CLAHE to bring out more features and contrast in the tumor's more nuanced areas. Tumour segmentation using a U-Net design subsequently enables pinpoint localisation of cancerous tissue. To further optimise learning and model convergence, a CNN classification network is used with a Stochastic Gradient Descent with Momentum optimiser. After following the aforementioned procedures, an end-to-end implementation was evaluated on a dataset consisting of 1,000 pancreatic CT images in MATLAB. A convolutional neural network (CNN) optimised using Stochastic Gradient Descent with Momentum, U-Net for segmentation, and CLAHE-based augmentation, all implemented in MATLAB. SGDM CLAHE is used for preprocessing. By improving picture quality, accurately defining tumour borders, and consistently recognising the existence of a tumour, this framework will aim to overcome several constraints of computed tomography imaging. To help radiologist make quick and accurate diagnosis, which improves patients' survival rates, this study will utilise these methodologies to guarantee a strong solution that will lead to early pancreatic tumour identification. Enhanced convolutional neural network (CNN) categorisation offers a quick, automated, and reliable method that might become a valuable CAD tool for the early diagnosis of pancreatic tumours, leading to better clinical outcomes.
