AUTHOR=Moustapha Maliki , Tasyurek Murat , Ozturk Celal 

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

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1646743

DOI=10.3389/frai.2025.1646743

ISSN=2624-8212

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 experiments: manual hyperparameter selection, k-fold retraining based on performance metrics, and the implementation of a genetic algorithm for hyperparameter optimization. The first involves training the models with manually selected hyperparameter sets (learning rate, batch size, and epoch). The second approach employs k-fold cross-validation to retrain the models based on the best-performing hyperparameter set. The third employed a genetic algorithm (GA) to automatically determine optimal hyperparameter values, selecting the model with the best performance on our dataset. We tested a Kaggle dataset with more than 5,000 samples and found ResNet18 to be the best model based on genetic algorithm-based hyperparameter selection. We also tested the proposed framework process on another separate public dataset and simulated adversarial attacks to ensure its robustness and dependability. The study outcomes had an accuracy of 99.57%, an F1-score of 99.50%, a precision of 99.44%, and an average AUC of 99.89 for each class. This study underscores the effectiveness of our proposed model, positioning it as a cutting-edge solution in COVID-19 x-ray image classification. Furthermore, the proposed study has the potential to achieve automatic predictions through the use of input images in a simulated web app. This would provide an essential supplement for imaging diagnosis in remote areas with scarce medical resources and help in training junior doctors to perform imaging diagnosis.