Detection of Pneumonia from Chest X-Ray Images

Abstract

Pneumonia is a dangerous which is caused by various viral agents. The diagnosis and treatment of pneumonia can be difficult because of the similarities with other lung diseases, which underscores the importance of chest x-rays for an early detection. This work presents techniques of pneumonia detection implementing CNNs, VGG16 and ResNet152V2 architectures, together with the Gradient Descent optimization method. The model is trained and tested on one of Kaggle's dataset which have 5,836 images that are labeled. This system automatically extract features from the chest X-Ray images and uses Gradient Descent optimization to improve its ability to differentiate between the pneumonia patients and healthy cases. For given dataset, the result provides accuracy of 96.56%, 95.34%, 92.9% and 94.23% for RestNet152V2,CNN,VGG16 and Gradient Descent respectively. Therefore this framework will facilitate to the detection of lung disease for experts and doctors as well. © 2024 IEEE.