saranyab21 / transfer-learning-based-pneumonia-detection

Transfer Learning-based Pneumonia Detection using Chest X-ray images

This study proposes an AI framework for the diagnosis of pediatric pneumonia using chest X-ray images. A convolutional neural network model is constructed to extract features from a given chest X-ray image and classify it to determine if a child is infected with pneumonia or not. Unlike traditional deep-learning classification tasks with sufficient image repositories, it is difficult to obtain a large amount of good-quality medical data for pediatric pneumonia classification tasks; therefore, data augmentation algorithms are employed, and Transfer Learning approaches are used to improve the validation and classification accuracy of the CNN model. Our proposed study uses different pre-trained network architectures (Resnet50, DenseNet, VGG16, InceptionV3) to reduce computation costs and achieves remarkable Recall (99.23%) and F1 Score (92.53%). Our study aims to ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier diagnosis, and resulting in improved clinical outcomes. In short, the key contribution of this work is to provide a CNN-based transfer learning approach using different pre-trained models to detect pediatric pneumonia and classify bacterial and viral pneumonia with higher accuracy compared to the recent state-of-the-art methods.