Pancreatic cancer detection using hyperspectral imaging and machine learning

Pancreatic cancer is a highly lethal disease, for which mortality is similar to incidence. Most patients with pancreatic cancer do not show symptoms until the disease has reached an advanced stage. The high mortality of pancreatic cancer is mainly due to fact that more than 50% of patients already discover it with metastasis, which reduces treatment options and chances of cure. The success of treatment depends on discovering disease as early as possible. Diagnosis in pancreatic cancer is traditionally confirmed by tissue biopsy of organ. This work presents a methodology to aid the diagnosis based on hyperspectral image for carcinogenic tissue classification using partial least squares and discriminant analysis to optimize process of diagnosing pancreatic adenocarcinoma. The results showed overlapping of areas classified by proposed model and by images used for diagnosis, proving to be a potential tool to aid in the diagnosis of pancreatic cancer.