AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs

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.

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

“…Such challenges can be addressed with robustly trained AI models and selection of appropriate cut-off values that maintain a good balance of sensitivity and specificity across different radiography units and radiographic quality. Although we did not compare the performance with other models in the literature, the AUCs from of our AI algorithm was similar to those reported for other models assessed with open access CXR datasets (https://nihcc.app.box.com/v/ChestXray-NIHCC/file/220660789610) (20). Apart from detection of radiographic findings assessed in our study with an AI algorithm, other studies have also assessed applications of AI for prioritizing interpretation of CXRs to expedite reporting of abnormal CXRs and specific findings (21).…”

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images

“…Similarly, Makris A. et al [43,44] used five different pre-trained CNN models and achieved 95% accuracy. Arora, R. et al [45] proposed stochastic deep learning model using ensemble of slandered convolutional models and evaluate developed model on standard dataset contain three classes: COVID-19, normal and pneumonia and attain an accuracy and AUC of 0.91 and 0.97, respectively. A detailed comparison is illustrated in Figure 6 and Table 6.…”

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

“…Although we did not compare the AI’s performance with other models from the literature, the AUCs of our AI algorithm were similar to those reported for other models assessed using open access CXR datasets ( (accessed on 4 August 2022)) [ 25 ]. Apart from detection of radiographic findings assessed in our study with an AI algorithm, other studies have assessed applications of AI for prioritizing interpretation of CXRs in order to expedite reporting of abnormal CXRs and specific findings [ 26 ]. Baltruschat et al reported the use of AI-based worklist prioritization for a substantial reduction in reporting turnaround time for critical CXR findings [ 27 ].…”