Pancreatic Tumor Recognition from CT Images through Advanced Deep Learning Techniques

Cancer constitutes a major affection nowadays, leading to death in many situations. Pancreatic malignant tumors represent the fourth most frequent cause of cancer related death in United States and Europe. The most trustworthy cancer diagnosis method is the biopsy, but this technique raises several risks, the most relevant one being the danger of tumoral spread through the human body. Thus, in the context of our research, we employ computerized methods for achieving highly accurate abdominal tumor recognition and segmentation within medical images. In the current approach, we performed the automatic recognition of pancreatic malignant tumors within Computed Tomography (CT) images, with the aid of Convolutional Neural Networks (CNN), by employing last generation architectures, as well as their improved versions, respectively combinations of these structures at classifier and decision level. The classification performance was assessed through specific metrics, an accuracy above 98% being achieved.