Efficient Heterogeneous Medical Image Data Handling for Lung Cancer Initial Screening using Deep Learning Techniques

Abstract

Medical image processing in lung cancer is a complex task due to its immense impact of medical image data components associated with essential bond of lung data characteristics so that nothing will be eliminated without proper care.  The generation, storage and analysis of lung cancer data from heterogeneous resources will be properly handled by the efficient approach in order to perform the optimal medical services for better cure.  Deep learning plays the vital role in simulating multi layered brain network of experts with the usage of machine learning for handling this complex lung cancer image data in an efficient way.   This paper presents the efficient heterogeneous medical image data handling for lung cancer initial screening using deep learning techniques.  This paper concentrates on the efficient selection of deep learning approach for taken care of the complex dimensional multi resource lung cancer data units towards the best tuning in the medical field service.  The future extension of this paper focuses on a real time lung cancer data interpreter model to access the lung cancer data directly using deep learning approaches.