Automated Diagnosis of Covid-19 and Pneumonia Using Transfer Learning and Custom Segmentation on Chest X-Ray Images

The key goals of this study are to discover, demonstrate, and quantify advancements in deep learning approaches for classifying healthy, pneumonia of the community-acquired and viral types, and COVID-infected lungs from X-ray images and to learn how the pre-trained models react to the training with custom segmented images. The proposed model uses the dataset pre-processed to generate unique masks and segment the lung region to train a convolutional neural network with a transfer learning model using VGG16 and VGG19 architecture. The accuracy and F1 score results for 3-way classification with custom processing are high for VGG19 with custom segmentation. In contrast, the results for the 4-way classification were stable with and without custom processing for both VGG16 and VGG19 models.