Prediction of Pneumonia and COVID-19 Using Deep Neural Networks

TL;DR

This study demonstrates that deep neural networks, especially DenseNet121, can accurately diagnose pneumonia from chest X-ray images with over 99% accuracy, aiding rapid detection and containment.

Contribution

It evaluates multiple deep learning models for pneumonia detection from X-rays and identifies DenseNet121 as the most effective, advancing medical image analysis techniques.

Findings

DenseNet121 achieves 99.58% accuracy in pneumonia detection.

Deep learning models outperform traditional methods in X-ray analysis.

Model performance is assessed using confusion matrices and accuracy metrics.

Abstract

Pneumonia, caused by bacteria and viruses, is a rapidly spreading viral infection with global implications. Prompt identification of infected individuals is crucial for containing its transmission. This study explores the potential of medical image analysis to address this challenge. We propose machine-learning techniques for predicting Pneumonia from chest X-ray images. Chest X-ray imaging is vital for Pneumonia diagnosis due to its accessibility and cost-effectiveness. However, interpreting X-rays for Pneumonia detection can be complex, as radiographic features can overlap with other respiratory conditions. We evaluate the performance of different machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, using chest X-ray images of pneumonia patients. Performance measures and confusion matrices are employed to assess and compare the models. The findings reveal that DenseNet121 outperforms other models, achieving an accuracy rate of 99.58%. This study underscores the significance of machine learning in the accurate detection of Pneumonia, leveraging chest X-ray images. Our study offers insights into the potential of technology to mitigate the spread of pneumonia through precise diagnostics.