## 1 Introduction  
Over the past few years, there has been a surge in studies exploring deep learning techniques to diagnose COVID-19 and pneumonia. Systematic reviews of AI-enabled COVID-19 detection can be found in [1; 2; 3] in 2021, [4; 5; 6; 7; 8; 9; 10] in 2022, and [11; 12; 13] in 2023. While all reviews delved into deep learning neural networks and medical image databases, there were apparent shifts in focus, from the feasibility of deep learning to this aim and limited databases in 2021, to model performance comparison and database survey in 2022, and to model enhancement/development and real-world applications recently. Because COVID-19 medical images were still limited in 2021, prior studies often focused on demonstrating the feasibility of deep learning models in distinguishing COVID-19 cases from normal subjects or patients with other respiratory diseases, such as pneumonia [14; 15; 16]. These studies either used chest X-ray images or CT scans [17; 18; 19; 20; 21; 22] and were designed as either binary or multi-class classifications [23; 24]. In general, the model classification accuracy was higher using chest X-ray images (90%+) than using CT (~85%) and was higher (1-5%) in binary than multi-class classifications [10; 25; 26]. To remedy data shortages, various techniques of data augmentation were used, including scaling, rotation, shifting, and image filtering such as grayscale and Gaussian blur [27; 28; 29]. With the increasing availability of COVID-19 X-rays and CT scans since 2021, research has pivoted to comparing various machine learning models and developing new models tailored for COVID-19 detection. For instance, [PERSON] et al. [30] considered 20 CNN models for COVID-19 detection, including EfficientNet-B5, DenseNet169, InceptionV3, ResNet-50, and VGG16, using 4173 CT images, and demonstrated that EfficientNet-B5 and ResNet-50 were persistently superior in accuracy and sensitivity, followed by DenseNet, EfficientNet, and Xception, while VGG-19 remained the lowest across models. However, mixed results were frequently reported by other studies. [PERSON] et al. [31] compared ResNet-50, InceptionV3, VGG-19, and Xception using CT scans and X-ray images and reported that VGG-19 performed best on CT scans (87% accuracy) and Xception performed best on X-ray images (98% accuracy). In another study using CT images [32], ResNet-50 had the highest accuracy (96.97%), followed by Xception (90.71%), Inception-v3 (89.38%), and VGG16 (87.12%). Considering that such variability was reflective of the inherent advantages and setbacks of CNN models, hybrid or ensemble models leveraging multiple networks have been explored. By combining Inception V3 with VGG16, [PERSON] et al. [33] achieved a 98% accuracy of COVID-19 prediction using 243 X-ray images, which outperformed Inception V3, VGG16, ResNet-50, DenseNet121, and MobileNet when tested individually. Similarly, [PERSON] et al. [34] integrated features extracted from Xception, MobileNetV2, and NasNetMobile and made the classification via a confidence fusion method. A notable variance in prior studies was the reported classification accuracies, ranging broadly from 78% to 100%, presumably due to the size or quality of the training/testing datasets. For instance, in 2021, [PERSON] et al. [35] reported 99.9% classification accuracy for VGG-19 and ResNet-50. However, only 263 original X-ray images (including 56 normal, 49 COVID-19, and 128 pneumonia) were used, which were increased to 3325 images for each category through augmentation techniques like flipping, rotation, and shifting. In 2022, using 4326 chest X-ray images, [PERSON] et al. [36] reported an accuracy of 100% for binary classification (normal vs. COVID-19) and 98.82% for multi-class classification (normal, COVID-19, pneumonia). On the other hand, 78% accuracy using VGG-19 and 4137 CT images was reported by [PERSON] et al. [30]. Despite exhibiting high accuracies in training and validation, CNN-based transfer learning might underperform during the testing phase [37; 38; 39; 40; 41].