<scp>LiteCovidNet</scp>: A lightweight deep neural network model for detection of <scp>COVID</scp>‐19 using X‐ray images

Kumar, Sachin; Shastri, Sourabh; Mahajan, Shilpa; Singh, Kuljeet; Gupta, Surbhi; Rani, Rajneesh; Mohan, Neeraj; Mansotra, Vibhakar

doi:10.1002/ima.22770

Cited by 15 publications

(12 citation statements)

References 78 publications

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“…The authors achieved accuracy scores of 98.7% and 98.2% for dataset 1 and dataset 2, respectively. Kumar et al [ 25 ] proposed DL network called “LiteCovidNet” to detect COVID-19 cases as the binary class (COVID-19 vs. normal) and the multi-class (COVID-19 vs. normal and pneumonia) using CXR images. Their method achieved an accuracy of 100% and 98.82% for binary and multi-class classification, respectively.…”

Section: Related Workmentioning

confidence: 99%

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture

Chetoui

Akhloufi

Bouattane

et al. 2023

Viruses

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COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. The model was customized and trained on five datasets containing more than 15,000 CXR images (including 4148COVID-19-positive cases) and then tested on 321 images (150 COVID-19-positive) from Montfort Hospital. Twenty percent of the data from the five datasets were used as validation data for hyperparameter optimization. Each CXR image was processed by the model to detect COVID-19. Multi-binary classifications were proposed, such as: COVID-19 vs. normal, COVID-19 + pneumonia vs. normal, and pneumonia vs. normal. The performance results were based on the area under the curve (AUC), sensitivity, and specificity. In addition, an explainability model was developed that demonstrated the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease. The fine-tuned RegNetX032 model achieved an overall accuracy score of 96.0%, with an AUC score of 99.1%. The model showed a superior sensitivity of 98.0% in detecting signs from CXR images of COVID-19 patients, and a specificity of 93.0% in detecting healthy CXR images. A second scenario compared COVID-19 + pneumonia vs. normal (healthy X-ray) patients. The model achieved an overall score of 99.1% (AUC) with a sensitivity of 96.0% and specificity of 93.0% on the Montfort dataset. For the validation set, the model achieved an average accuracy of 98.6%, an AUC score of 98.0%, a sensitivity of 98.0%, and a specificity of 96.0% for detection (COVID-19 patients vs. healthy patients). The second scenario compared COVID-19 + pneumonia vs. normal patients. The model achieved an overall score of 98.8% (AUC) with a sensitivity of 97.0% and a specificity of 96.0%. This robust deep learning model demonstrated excellent performance in detecting COVID-19 from chest X-rays. This model could be used to automate the detection of COVID-19 and improve decision making for patient triage and isolation in hospital settings. This could also be used as a complementary aid for radiologists or clinicians when differentiating to make smart decisions.

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Section: Related Workmentioning

confidence: 99%

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture

Chetoui

Akhloufi

Bouattane

et al. 2023

Viruses

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“…AlexNet was groundbreaking in its use of GPUs for training deep neural networks, while ResNet-50 introduced residual connections between different layers to improve gradient flow and enable the training of even deeper neural networks [34,35]. MobileNet was chosen for its simpler architecture and smaller computational requirements [36][37][38][39]. It will be desirable to run a computer-aided diagnostic (CAD) system on a personal computer or even a smartphone, provided it can achieve sufficient diagnostic accuracy.…”

Section: Selection Of Cnn Modelsmentioning

confidence: 99%

“…However, only 263 original X-ray images (including 56 normal, 49 COVID-19, and 128 pneumonia) were used, which were increased to 3325 images for each category through augmentation techniques like flipping, rotation, and shifting. In 2022, using 4326 chest X-ray images, Kumar et al [36] reported an accuracy of 100% for binary classification (normal vs. COVID-19) and 98.82% for multi-class classification (normal, COVID-19, pneumonia). On the other hand, 78% accuracy using VGG-19 and 4137 CT images was reported by Garg et al [30].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

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“…Tiwari et al 43 propose a lightweight capsule network architecture for the detection of COVID‐19 from lung CT scans. Kumar et al 44 propose the LiteCovidNet, which is a lightweight DNN model for the detection of COVID‐19 via x‐ray images. Aslan 45 proposes CoviDetNet, in which the authors design a lightweight CNN architecture trained from scratch with CXR images.…”

Section: Lightweight Ai Modelsmentioning

confidence: 99%

Fighting against COVID‐19: Innovations and applications

Zhang

2023

Int J Imaging Syst Tech

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LiteCovidNet: A lightweight deep neural network model for detection of COVID‐19 using X‐ray images

Cited by 15 publications

References 78 publications

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Fighting against COVID‐19: Innovations and applications

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