Pancreatic Cancer Classification using Deep Learning

Abstract

The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. However, these methods are no longer effective (TWSVM). However, these strategies do not deliver an accurate performance. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, which are not always accurately classified; in contrast, the classification in this one is determined by looking at the genetic data of the patient. An accurate number can be obtained by using the blood and urine samples collected from patients since these samples were utilized to construct the genetic data. With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used.