Comparative Analysis of Lung Sac Inflation

Inflated lung sacs are a serious medical condition that may be fatal to people. An infectious agent, most likely a virus or bacterium, is responsible for this illness. According to the WHO (World Health Organization), lung sac inflation is the third leading cause of mortality in India; a virus, or bacterium, is responsible for this illness. Expert radiotherapists are required to read chest X-rays for pneumonia diagnosis. It’s a painful and tough process to breathe due to an illness. As lung sac inflation is a potentially life-threatening respiratory condition, its early detection is of paramount importance. We provide a methodical approach for pneumonia detection that learns from digital chest X-ray pictures to reliably identify pneumonic lungs. The medical community will benefit greatly from this. We compared the accuracy using five different machine learning models namely random forest, KNN (K-nearest neighbors), CNN (convolutional neural networks), and decision tree of machine learning methods. The CNN model achieved an accuracy of 91.8% in general. The purpose of this project is to predict pneumonia and try to improve the accuracy.