Pancreatic Cancer Detection and Classification using Deep Learning
N Saritha, B P Bhagya, M Hruthika, R.P.P. Kanchana, S M Sudharani, G K Shruthi · 2025
Pancreatic Cancer is among the deadliest cancers, characterized by late diagnosis and a poor prognosis. This paper provides a review of the application of deep learning techniques in the early detection of pancreatic cancer, utilizing high-level image processing algorithms and convolutional neural networks (CNNs) to process medical imaging data. The model developedin this paper, based on a heterogeneous dataset, aims to improve diagnostic accuracy and enable early intervention. The results reveal that deep learning can be effectively used for better early detection of pancreatic cancer, thereby improving patient prognosis. Pancreatic carcinoma is a multi factorial neoplasm, and prognostication depends on the tumor’s origin and the clinical presentation stage. Pancreatic adenocarcinomas arise from the exocrine pancreas and are the fourth most common cause of cancer-related deaths in the United States. In contrast, well-differentiated pancreatic neuroendocrine tumors (pNETs) of the endocrine pancreas are uncommon and are characterized by slow-growing tumors withagood prognosis. The only potentially curative treatment is surgical resection, and proper assessment of respectability is critical to avoid futile therapy.