Fuzzy Sematic Segmentation and Efficient Classification of Lung Cancer Multi-Dimensional Datasets

Abstract

Lung cancer is one of the leading cause of cancer death around the world. Lung cancer has been the most common cancer worldwide since 1985, both in terms of incidence and mortality. Recognition and prediction of lung cancer at the earliest stage can be very useful to improve the survival rate of patients. Effective and early diagnosis of cancer is one the major challenging task for medical practitioners. In this research work, we propose a novel technique on lung MRI image based segmentation and classification is using fuzzy logic and deep learning. The proposed technique considers multi-dimensional medical dataset modeling and representation for effective diagnosis and prediction. A fuzzy based sematic segmentation with relevance to Region of Interest (RoI) extraction and append deep learning models to customized RoI selection under segmented patches. The multi-layer classification approach is viewed to be an effective and accurate diagnosis method for the prediction of disease at early stage.