A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans

Hasija, Sanskar; Akash, Peddaputha; Hemanth, Maganti Bhargav; Kumar, Ankit; Sharma, Sanjeev

doi:10.1016/j.neuri.2022.100069

Cited by 23 publications

(4 citation statements)

References 32 publications

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“…To discuss the effectiveness of the proposed method, we performed a contrastive analysis of the two datasets—the COVIDx CT-2A dataset and the medium-sized dataset. First, we tested the COVIDx CT-2A dataset to obtain evaluation metrics and compared it with VGG19 and the state-of-the-art methods proposed by Fan et al [ 35 ], Ter-Sarkisov [ 36 ], Yang et al [ 37 ], Hasija et al [ 51 ], and Chetoui et al [ 38 ]. The experimental results depicted in Table 4 demonstrate that the improved VGG19 model outperforms the original VGG19 model in all metrics.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

2023

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The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers.

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Section: Experiments and Resultsmentioning

confidence: 99%

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

2023

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“…The CNN based model is proposed for multi-class classification of chest CT scans into normal, COVID-19, and pneumonia. The proposed work obtained an average accuracy of 98% [48] .…”

Section: Related Workmentioning

confidence: 89%

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices

Ahuja

Panigrahi

Dey³

et al. 2022

Applied Soft Computing

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“…With the MobileNetV3Large model, the suggested Framework achieves the highest accuracy of 99.74%. Hasija Sanskar [ 16 ] performed multiclassification on CT images using various CNN models, then binary classification on COVID-positive and COVID-negative CT images in the first phase, and then COVID negative images were categorized into Pneumonia positive and Pneumonia negative in the second phase. In comparison to existing pre-trained models, the proposed method obtained 98.38 percent accuracy.…”

Section: Related Workmentioning

confidence: 99%

Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation

Khan

Gong

et al. 2023

J. Imaging

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A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.

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A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans

Cited by 23 publications

References 32 publications

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices

Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation

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