Dr. Dipali Shende,Parth Dange,Bhairavi Adchule ,Unmesh Chaudhari

Pancreatic cancer is one of the deadliest forms of cancer, with a low survival rate due to late diagnosis. Early detection and precise staging are crucial for improving treatment outcomes and patient survival. This paper presents a comprehensive review of a deep learning-based solution that integrates Convolutional Neural Networks (CNNs) for pancreatic tumor detection and cancer probability prediction. The proposed system leverages Endoscopic Ultrasound (EUS) imaging datasets to train a binary classification model for tumor detection and a multi-output model for predicting the probability of early-stage cancer progression. The system aims to assist healthcare providers in identifying high-risk patients, enabling timely intervention and personalized treatment planning. Keywords— Pancreatic cancer, tumor detection, imaging techniques, biomarkers, Convolutional Neural Network (CNN), You Only Look Once (YOLO), liquid biopsy, tumor simulation, multiscale modeling, cancer risk prediction, machine learning, early detection.

https://doi.org/10.58357/jedcs.2024.v3.i4.1