COVID-19 detection in X-ray images using convolutional neural networks

Arias-Garzón, Daniel; Alzate-Grisales, Jesús Alejandro; Orozco-Arias, Simón; Arteaga-Arteaga, Harold Brayan; Bravo-Ortíz, Mario Alejandro; Mora-Rubio, Alejandro; Saborit-Torres, Jose Manuel; Serrano, Joaquim Ángel Montell; Vayá, María de la Iglesia; Cardona-Morales, Oscar; Tabares-Soto, Reinel

doi:10.1016/j.mlwa.2021.100138

Cited by 77 publications

(42 citation statements)

References 19 publications

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“…Due to the advancement of different deep neural network, such as ResNet18, ResNet50 [ 30 ], AlexNet [ 31 ], DenseNet [ 32 ], and VGG16 [ 33 ], we extracted techniques from these models, and were able to make our model more error-free, sophisticated, and accurate. Similar to our study, another recent study [ 53 ] applied the method of preprocessing for lung segmentation, which removed the unwanted surroundings of the lung X-rays and kept the desired lung tissues only. By this process, the authors achieved a high detection accuracy rate of 97% for COVID-19.…”

Section: Discussionmentioning

confidence: 89%

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

Chakraborty

Murali

Mitra

2022

IJERPH

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The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.

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Section: Discussionmentioning

confidence: 89%

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

Chakraborty

Murali

Mitra

2022

IJERPH

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“…Some scholars use image normalization, data augmentation and image resizing for image preprocessing [ 5 , 6 ]. For chest X-ray image classification, related researches use methods such as deep learning, transfer learning, and ensemble learning.…”

Section: Introductionmentioning

confidence: 99%

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Huang

Liao

2022

Computers in Biology and Medicine

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“…Arias-Garzn et al [ 15 ] used popular deep learning models (VGG19 and U-Net) to classify CXR images as positive or negative COVID-19. First, the authors used U-Net [ 14 ] for lung segmentation in order to remove the surroundings which do not offer relevant information for the task.…”

Section: Introductionmentioning

confidence: 99%

Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays

Chetoui

Akhloufi

2022

JCM

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The rapid spread of COVID-19 across the globe since its emergence has pushed many countries’ healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19.

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COVID-19 detection in X-ray images using convolutional neural networks

Cited by 77 publications

References 19 publications

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

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

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays

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