Classification of Pneumonia using InceptionNet, ResNet and CNN

Abstract

Pneumonia is a respiratory infection resulting in inflammation of the Lungs. Viruses, bacteria, or fungi could be to blame for this infection's sickness. Pneumonia can range in intensity from non-threatening to fatal. The most vulnerable demographics include infants and young children, seniors, those with health issues, and people with weakened immune systems. Early detection of pneumonia disease is essential for ensuring curative care and boosting survival rates. The most common approach for diagnosing pneumonia is a chest x-ray. However, the examination of chest radiographs is a subjective and difficult task. In this study, Convolutional Neural Network, Resnet, Inception Net was used for the analysis, as a result by comparing the above-mentioned models we got the accuracy more than 90% for Inception Neural Network. By these results new model has come up with the accuracy of the more than 90%. Future work includes the comparison of other models, among them Models with highest accuracy is chosen to design and develop the Dash Stream Application for the prediction of Pneumonia using X-ray images.

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