Early Diagnosis of Pneumonia with Deep Learning

TL;DR

This paper presents a deep learning approach using CNNs and residual networks for early pneumonia detection from X-ray images, achieving higher accuracy than previous methods.

Contribution

It introduces a novel deep learning architecture with image preprocessing for early pneumonia diagnosis from X-ray images, improving classification accuracy.

Findings

Achieved 78.73% accuracy in pneumonia detection

Surpassed previous top accuracy of 76.8%

Demonstrated effectiveness of residual networks in medical imaging

Abstract

Pneumonia has been one of the fatal diseases and has the potential to result in severe consequences within a short period of time, due to the flow of fluid in lungs, which leads to drowning. If not acted upon by drugs at the right time, pneumonia may result in death of individuals. Therefore, the early diagnosis is a key factor along the progress of the disease. This paper focuses on the biological progress of pneumonia and its detection by x-ray imaging, overviews the studies conducted on enhancing the level of diagnosis, and presents the methodology and results of an automation of xray images based on various parameters in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and residual network architecture for classifying the images. Our findings yield an accuracy of 78.73%, surpassing the previously top scoring accuracy of 76.8%.