Classification of Medical Images Using Deep Learning to Aid in Adaptive Big Data Crowdsourcing Platforms

Abstract

Deep Learning has radically transformed the field of Medical Image Analysis by surpassing human performance in several challenging classification tasks and thus has widespread applications in industry and academia. In this work, we employ a Deep Convolutional Neural Network (DCNN) that uses Transfer Learning and is trained using the Open-access Medical Image Repositories and OpenI repository. We use DCNN to assist in a crowdsourced Big Data platform by conducting two crucial classification tasks: (1) Interclass classification of the type of the medical image—MRI, Ultrasound or X-Ray; (2) Intraclass classification of the X-Ray images into chest or abdomen X-Rays. Our approach automates the classification process of medical images, to provide certified medical experts with medical images of individuals on a crowdsourced platform, along with similar medical images of the suspected medical condition for validation, in order to enhance diagnostic effectiveness. In this work, we focus primarily on the classification tasks using DCNN; the elucidation of the privacy-preserving architecture of our cloud-based crowdsourcing platform and classification using conventional clustering approaches in the absence of large training images is deferred to future work.