Performance Analysis of Deep Learning Network Models of Localized Images in Chest X-ray Decision Support System

Abstract

Nowadays, the implementation of deep learning in various fields of data mining and big data analytics has been widely used in many applications. This happens because of the ability of deep learning to be able to have excellent performance in terms of predicting cases of classification, features engineering, and clustering. Images data or texts that have large dimensions can also be processed iteratively by deep learning. In the medical field, deep learning is widely used as computer-aided detection (CAD) to provide a decision support system for radiologists or practitioners. In this research, we try to do a deep performance evaluation of several deep learning network models for the Chest X-ray disease decision support system (DSS). These X-ray images are enormous, it consists of 110,120 images, and the size is about 44 GB. Our primary interest is to get a detailed performance profile for every deep learning network model. It consists of a few evaluation aspects, accuracy performance by using areas under the receiver operating characteristic (AUROC), evaluation of training and testing time, investigation of memory usage, observation of central processing unit (CPU) usage, graphics processor power consumption, and provide some improvement solutions. We also offered a few solutions and suggestions to help the doctor or practitioner to select the most effective deep learning network model.