COVID-19 image classification using deep learning: Advances, challenges and opportunities

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network

“…Among the magnetic resonance imaging data, CX-Rs are highly utilised to observe signs of COVID-19. This study explored the merits afforded through the usage of iteratively ensemble pruned deep learning (DL) models towards differentiating the CX-Rs exploring COVID-19 pneumonia associated transparencies from normal and bacterial pneumonia utilising publicly accessible CX-R collections ( Aggarwal et al, 2022 ). Performance of the introduced model has been evaluated and weights have been assigned that rely on its predictions ( Rajaraman et al, 2020 ).…”

A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia

“…A study by [ 17 ] systematically reviewed publications of machine learning models for the diagnosis or prognosis of COVID-19 from X-ray or CT images, concluding that all identified models had methodological flaws and/or underlying biases preventing their use in clinical practice. A review by [ 18 ] identified that most of the studies have utilized small datasets and lacked comparative analysis with other existing research, and the codes and data were not available. In our review, we also identified fundamental problems that limit the adoption of algorithms in healthcare centers.…”

ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects

“…Several deep learning models have been developed to diagnose or predict a disease in early stage using the signals generated from human body such EEG, ECG, and non-invasive images [ [10] , [11] , [12] , [13] , [14] ]. Antczak [ 15 ] trained an Inception network, generated synthetic ECG data from time-domain Wasserstein GAN, and trained a denoising encoder to perform ECG denoising.…”