Medical Image Processing using Deep Learning Techniques in Big Data Perspective

Abstract

Artificial intelligence and machine learning will be the driving forces behind the next computing revolution. These technologies rely on the ability to identify trends from historical information and predict future outcomes. One of the best machine learning techniques, deep learning is employed in a variety of applications, including object recognition, picture categorization, image analysis, and clinical archives. Image and video data are necessary for both diagnosing the patient's illness and determining its severity. Convolutional neural networks are efficient gears for digital picture classification and image understanding. The production of medical photographs has ex-ponentially increased as a result of the proliferation of digital devices and the development of camera technology, which creates Bigdata. Massive, difficult-to-manage volumes of structured, unstructured data are referred to as "Big data". The more data processed for analysis, the greater will be the analytical accuracy and also the greater would be the confidence in our decisions based on the analytical findings. In this paper, we proposed a novel method for early detection of pneumonia disease using deep learning techniques along with the big data storage and big data analytics to achieve more better performance. The results show that, the model achieved 91.16% of accuracy and 93.22% of F1-score.