WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 12, 2024
Automated Diagnosis of Covid-19 and Pneumonia Using Transfer Learning and Custom Segmentation on Chest X-Ray Images
Authors: ,
Abstract: 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.
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Keywords: Lung Segmentation, Transfer Learning, Chest X-Ray Image, Pneumonia, COVID-19, Image Processing
Pages: 328-335
DOI: 10.37394/232018.2024.12.32