Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.

ABSTRACT: Deep learning, a subset of artificial intelligence, has made remarkable strides in computer vision, particularly in addressing challenges related to medical images. Deep transfer learning (DTL), one of the techniques of deep learning, has emerged as a pivotal technique in medical image analysis, including studies related to COVID-19 detection and classification. Our paper proposes an alternative DTL framework for classifying COVID-19 x-ray images in this context. Unlike prior studies, our approach integrates three distinct experimentation processes using pre-trained models: AlexNet, EfficientNetB1, ResNet18, and VGG16. Furthermore, we explore the application of YOLOV4, traditionally used in object detection tasks, to COVID-19 feature detection. Our methodology involves three different expe

SUBMITTER: Moustapha M 

PROVIDER: S-EPMC12555394 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature