SARS-CoV-2 Detection in Genosensor Images via Deep Learning and Data Augmentation

This work addresses the challenge of classifying genosensor images obtained by scanning electron microscopy (SEM) for SARS-CoV-2 diagnosis. Due to the specialized nature of the data, image datasets are inherently small, which limits the effectiveness of deep learning models. To overcome this limitation, we investigated the impact of seven data augmentation strategies - Flipping, Shift, Random Erasing, AugMix, AutoAugment, RandAugment and TrivialAugment - on the performance of four Convolutional Neural Networks (CNNs): ResNet50, ResNet101, DenseNet121, and ConvNeXt-Tiny. Our analyses demonstrated that data augmentation is crucial for the success of the task, achieving a remarkable accuracy of 97.85%, surpassing both traditional feature extraction methods and previously tested deep learning approaches. Furthermore, our findings indicate that the choice of augmentation technique is critical, with aggressive methods proving detrimental for highly specialized microscopy data.