Pancreatic tumor detection by convolutional neural networks - Zavalsız, Muhammed Talha; Alhajj, Sleiman; Sailunaz, Kashfia; Özyer, Tansel; Alhajj, Reda (Institute of Electrical and Electronics Engineers Inc., 2022)
Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection.
Keywords: Convolutional Neural Networks; Deep Learning; Flask; Medical Image; Pancreatic Ductal Adenocarcinoma; Pancreatic Tumor Detection