Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients

Pathak, Yadunath; Shukla, Piyush Kumar; Arya, K. V.

doi:10.1109/tcbb.2020.3009859

Cited by 79 publications

(63 citation statements)

References 24 publications

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“…They also experimented with other models like GoogleNet, VGG16, Alexnet, Decision Trees, and AdaBoost classifiers. Researcher in [33] have employed the Bi-LSTM mixture density network (DBM) for COVID classification. In this work, Memetic Adaptive Differential Evolution (MADE) is used for hyper-parameters tuning of the DBM model, where the layer number, node number, clip gradients, learning rate, number of epochs, and batch size were fine-tuned.…”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards Computed Tomography (CT) scans. Efforts are being made to design deep learning models capable of COVID-19 detection using CT scans. This has certainly reduced the manual intervention in disease detection but reported accuracy is limited. Methods: The present work proposes an automatic system for COVID-19 diagnosis based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier. Typically, features are extracted from the fully connected layer of transfer learned MobileNetv2 that are then employed for FKNN training. The hyper-parameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-arts. Conclusion: The outcomes presented in the paper will aid in speedy detection of this virus at the different phases. Such a detection system will also increase the opportunities for fast recovery of patients worldwide thereby releasing the pressure off the medical professionals and the healthcare system around the world.

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Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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“…The model achieved an accuracy of 98.37%, AUC of 98.2%, sensitivity of 98.87%, precision of 98.74%, and F1 score of 98.14%. As it can be noted from Table 6 that the authors of Soares et al (2020), Panwar et al (2020) and Pathak, Shukla & Arya (2020) have utilized different individual CNNs networks. These authors did not neither fuse several CNNs architectures nor combine DL features with handcrafted features.…”

Section: Discussionmentioning

confidence: 99%

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Ragab¹,

Attallah²

2020

PeerJ Computer Science

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The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

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“…Four class chest-CT scanned images are collected from various sources such as COVID-19 [43], Pneumonia [44,45], and Tuberculosis [13,46]. We have collected group of chest CT scanned images as 2373 images of COVID-19 infected patients, 2890 pneumonia infected patients, 3193 tuberculosis images and 3038 healthy subjects.…”

Section: Datasetmentioning

confidence: 99%

Densely connected convolutional networks-based COVID-19 screening model

2021

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The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

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Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients

Cited by 79 publications

References 24 publications

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Densely connected convolutional networks-based COVID-19 screening model

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