Using Deep Learning to Improve Early Diagnosis of Pneumonia in Underdeveloped Countries

TL;DR

This paper explores using a modified deep learning convolutional neural network to diagnose pneumonia from X-ray images, aiming to improve early detection in underdeveloped countries with limited medical resources.

Contribution

It demonstrates that a deep learning model can achieve high accuracy, sensitivity, and specificity in pneumonia diagnosis from X-ray images, offering a potential tool for resource-limited settings.

Findings

Average accuracy of 82.5% in identifying abnormal lungs

Maximum specificity of 98.5% achieved

Maximum sensitivity of 90% achieved

Abstract

As advancements in technology and medicine are being made, many countries are still unable to access quality medical care due to cost and lack of qualified medical personnel. This discrepancy in healthcare has caused many preventable deaths, either due to lack of detection or lack of care. One of the most prevalent diseases in the world is pneumonia, an infection of the lungs that killed 2.56 million people worldwide in 2017. In this same year, the United States recorded a pneumonia death rate of 15.88 people per 100000 in population, while much of Sub-Saharan Africa, such as Chad and Guinea, experienced death rates of over 150 people per 100000. In sub-Saharan Africa, there is an extreme shortage of doctors and nurses, estimated to be around 2.4 million. The hypothesis being tested is that a deep learning model can receive input in the form of an x-ray and produce a diagnosis with the equivalent accuracy of a physician, compared to a prediagnosed image. The model used in this project is a modified convolutional neural network. The model was trained on a set of 2000 x-ray images that have predetermined normal and abnormal lung findings, and then tested on a set of 400 images that contains evenly split images of pneumonia and healthy lungs. For each computer-run test, data was collected on a base measurement of accuracy, as well as more specific metrics such as specificity and sensitivity. Results show that the algorithm tested was able to accurately identify abnormal lung findings an average of 82.5% of the time. The model achieved a maximum specificity of 98.5% and a maximum sensitivity of 90% separately, and the highest simultaneous values of these two metrics was a sensitivity of 90% and a specificity of 78.5%. This research can be further improved by testing other deep learning models as well as machine learning models to improve the metric scores and chance of correct diagnoses.