XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans - Addo, Daniel; Al-Antari, Mugahed A.; Zhou, Shijie; Sarpong, Kwabena; Butun, Ertan; Talo, Muhammed; Ukwuoma, Chiagoziem C. (Institute of Electrical and Electronics Engineers Inc., 2023)
The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.
7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 -- 26 October 2023 through 28 October 2023 -- Ankara -- 194332
Keywords: Artificial Intelligence; COVID-19 Diagnosis; Variational Auto-Encoder; Vision Transformer