tetano
Editor, Senior Moderator
Phys Eng Sci Med
. 2021 Oct 5.
doi: 10.1007/s13246-021-01060-9. Online ahead of print.
AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs
Ridhi Arora[SUP] #[/SUP][SUP] 1 [/SUP], Vipul Bansal[SUP] #[/SUP][SUP] 2 [/SUP], Himanshu Buckchash[SUP] #[/SUP][SUP] 1 [/SUP], Rahul Kumar[SUP] #[/SUP][SUP] 3 [/SUP], Vinodh J Sahayasheela[SUP] 4 [/SUP], Narayanan Narayanan[SUP] #[/SUP][SUP] 5 [/SUP], Ganesh N Pandian[SUP] 4 5 [/SUP], Balasubramanian Raman[SUP] 1 5 [/SUP]
Affiliations
Abstract
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
Keywords: COVID-19; Classification; Deep learning; Feature extraction; Image processing; Machine learning; X-ray.
. 2021 Oct 5.
doi: 10.1007/s13246-021-01060-9. Online ahead of print.
AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs
Ridhi Arora[SUP] #[/SUP][SUP] 1 [/SUP], Vipul Bansal[SUP] #[/SUP][SUP] 2 [/SUP], Himanshu Buckchash[SUP] #[/SUP][SUP] 1 [/SUP], Rahul Kumar[SUP] #[/SUP][SUP] 3 [/SUP], Vinodh J Sahayasheela[SUP] 4 [/SUP], Narayanan Narayanan[SUP] #[/SUP][SUP] 5 [/SUP], Ganesh N Pandian[SUP] 4 5 [/SUP], Balasubramanian Raman[SUP] 1 5 [/SUP]
Affiliations
- PMID: 34609703
- DOI: 10.1007/s13246-021-01060-9
Abstract
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
Keywords: COVID-19; Classification; Deep learning; Feature extraction; Image processing; Machine learning; X-ray.