Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection
It addresses COVID-19 detection from X-rays for medical diagnosis, but it is incremental as it applies existing methods to a new dataset without novel methodological contributions.
This study compared eight pre-trained neural networks for classifying X-ray images into normal, pneumonia, and COVID-19 categories, with DenseNet achieving the highest accuracy of 97.64% in multiclass classification and VGG, ResNet, and MobileNet reaching 99.98% precision in binary classification.
This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps.