An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

Chakraborty, Somenath; Murali, Beddhu; Mitra, Amal K.

doi:10.3390/ijerph19042013

Cited by 35 publications

(23 citation statements)

References 49 publications

(60 reference statements)

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“…A number of efforts have been focused on deep learning model development for medical image interpretation. These include the detection of SARS-CoV-2 using X-ray images [ 6 , 7 , 8 ], neurological disease progression in MRI images [ 9 , 10 , 11 ], and the identification of tumors using CT scans [ 12 , 13 , 14 ], among others. Advances in foreign body detection have been limited to X-ray imaging techniques identifying pathologies like tuberculosis, however high contrast items in chest cavities like coins, medical devices, jewelry can be confounding [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection

2022

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Tissue phantoms are important for medical research to reduce the use of animal or human tissue when testing or troubleshooting new devices or technology. Development of machine-learning detection tools that rely on large ultrasound imaging data sets can potentially be streamlined with high quality phantoms that closely mimic important features of biological tissue. Here, we demonstrate how an ultrasound-compliant tissue phantom comprised of multiple layers of gelatin to mimic bone, fat, and muscle tissue types can be used for machine-learning training. This tissue phantom has a heterogeneous composition to introduce tissue level complexity and subject variability in the tissue phantom. Various shrapnel types were inserted into the phantom for ultrasound imaging to supplement swine shrapnel image sets captured for applications such as deep learning algorithms. With a previously developed shrapnel detection algorithm, blind swine test image accuracy reached more than 95% accuracy when training was comprised of 75% tissue phantom images, with the rest being swine images. For comparison, a conventional MobileNetv2 deep learning model was trained with the same training image set and achieved over 90% accuracy in swine predictions. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification.

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Section: Introductionmentioning

confidence: 99%

Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection

2022

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“…Using the same dataset, the f1-scores of our method comparing with Rahman [ 46 ] and Lafraxocite [ 33 ] were improved by 1.11% and 1.97%, respectively. For dataset 3, the f1-scores in our technique were improved by 0.48%, 5.49%, 0.66%, and 4.46%, and the accuracies were gained by 0.40%, 6.91%, 8.41%, and 0.98% comparing the results of the articles of [ 7 , 8 , 30 , 34 ], respectively.…”

Section: Methodsmentioning

confidence: 93%

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Lasker

Ghosh²,

Obaidullah

et al. 2022

Multimed Tools Appl

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Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

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“…Chakraborty et al [ 10 ] presented a COVID-19 detection method based on a Deep Learning Method (DLM) using X-ray images. The authors used different architectures of deep neural networks in order to achieve optimal results.…”

Section: Related Workmentioning

confidence: 99%

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images

Giełczyk

Marciniak

Tarczewska

et al. 2022

JCM

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Background: This paper presents a novel lightweight approach based on machine learning methods supporting COVID-19 diagnostics based on X-ray images. The presented schema offers effective and quick diagnosis of COVID-19. Methods: Real data (X-ray images) from hospital patients were used in this study. All labels, namely those that were COVID-19 positive and negative, were confirmed by a PCR test. Feature extraction was performed using a convolutional neural network, and the subsequent classification of samples used Random Forest, XGBoost, LightGBM and CatBoost. Results: The LightGBM model was the most effective in classifying patients on the basis of features extracted from X-ray images, with an accuracy of 1.00, a precision of 1.00, a recall of 1.00 and an F1-score of 1.00. Conclusion: The proposed schema can potentially be used as a support for radiologists to improve the diagnostic process. The presented approach is efficient and fast. Moreover, it is not excessively complex computationally.

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An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

Cited by 35 publications

References 49 publications

Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection

Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images

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