Predictive System for early Detection and Classification of Pancreatic Cancer Using Deep Learning Techniques

Research and medical practice in the area of pancreatic cancer (PC) categorization and recognition depend heavily on medical image analysis. PC is difficult to diagnose and manage. There are numerous classifications for pancreatic cancer that can be used. The process of classifying pancreatic cancer will be finished by utilizing deep learning technologies. There are several approaches of classifying pancreatic cancer, and each one can be carried out by utilizing deep learning or machine learning technologies. Previously, techniques like Support Vector Machines (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines might be used to diagnose pancreatic cancer. However, these methods are no longer effective (TWSVM). However, these strategies do not deliver an accurate performance. The RIC-GD method is a novel machine learning approach proposed for the detection of pancreatic tumors. It utilizes an ensemble classifier to enhance the classification performance. The technique involves using a set of classifiers and determining the similarity measure between the training and testing samples to ensure accurate classification of the samples. The accuracy and specificity of the RIC-GD method have been evaluated and compared to Naive Bayes and decision tree methods.