Pneumonia detection with x-ray images approach with deep learning

Abstract

This study tries to create a reliable model for detecting pneumonia using chest X-ray images. The work makes use of advanced deep learning architectures such as DenseNet, InceptionV3, ResNet, VGG16, and VGG19, as well as Google Colab's tremendous computing capabilities and a Kaggle data set. To achieve high accuracy and reliability, the project goes through rigorous preprocessing, training, and validation processes. Key performance parameters, such as precision, recall, and F1-score, are used to assess each model's success. The findings show significant gains in detection accuracy, showing the utility of deep learning models in medical picture processing. This research gives useful insights for healthcare practitioners and contributes to the development of automated diagnostic systems aimed at improving pneumonia identification and treatment.