Effective Utilization of Multiple Convolutional Neural Networks for Chest X-Ray Classification

Abstract

Imaging for medical purposes (Medical Imaging) involves various technologies and processes that capture different areas/organs of the human or the animal body. These imaging modalities contain various types of visual representations corresponding to different structural and qualitative properties of the scanning area. These are very helpful in identifying and confirming a disease’s presence or keeping track of the progression of an already diagnosed disease. Radiography, Magnetic resonance imaging (MRI) and Ultrasonography are typical examples of Medical Imaging Technologies.

A Report by UNICEF in 2019 [1] shows that a child dies because of pneumonia every 39 s. It further shows that pneumonia causes more deaths, especially in children more than any other infectious disease. Tuberculosis is another dangerous disease like that. Even though deadly diseases like Pneumothorax have a comparatively low death rate, [2] shows that it has a high recurrence rate which can be very harmful. Most chest-related diseases can be controlled or sometimes completely cured if identified early and treated well. The most used method in diagnosing chest-related diseases is Chest Radiography. The advantages of chest radiography are the low cost and its convenient operation. Nevertheless, as shown in [3], confusions can occur when diagnosing chest-related diseases using chest radiography due to the complex structural representations in chest radiography images, which can differ from one person to another.