Prediction of Pneumonia and COVID-19 Using Deep Neural Networks
M. S. Haque M. S. Taluckder S. B. Shawkat M. A. Shahriyar M. A. Sayed C. Modak
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.