Pancreatic Cancer Prediction through Convolutional Neural Networks

Due to the late appearance of cancer-specific symptoms, initial diagnosis of the pancreatic cancer is difficult and absence of a dependable screening method to find patients at high risk. CT, MRI, and EUS scanning techniques are the most frequently used methods for detecting abnormalities in human pancreases. As a result, some accurate and automatic classification approaches are needed to lower the death rate in humans. To reduce the amount of time radiologists spend on cancer identification while retaining accuracy, many approaches are being explored. The methods for detecting pancreatic cancer using an MRI scan and an EUS are difficult and time consuming. These techniques are not widely used. In order to overcome these limitations and develop a method that is significantly more accurate, affordable, and useful for identifying pancreatic cancer, this study uses a variety of deep learning methods using convolution neural networks as well as other machine learning algorithms.