Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening

Shelke, Ankita; Inamdar, Madhura; Shah, Vruddhi; Tiwari, Amanshu; Hussain, Aafiya; Chafekar, Talha; Mehendale, Ninad

doi:10.1007/s42979-021-00695-5

Cited by 75 publications

(25 citation statements)

References 23 publications

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“…Inception-ResNet V2 provided 92.18% classification accuracy. Shelke et al [ 29 ] proposed a classification model for detecting COVID-19 using chest X-rays images. The accuracy obtained with DenseNet-161 is 98.9%, while ResNet-18 achieved 76% accuracy in severity classification.…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based framework for detecting COVID-19 patients using chest X-rays

et al. 2022

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.

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Section: Related Workmentioning

confidence: 99%

A deep learning-based framework for detecting COVID-19 patients using chest X-rays

et al. 2022

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“…A new hybrid feature selection method was proposed by Shaban et al [ 33 ], which combined both wrapper and filter feature selection methods. Almost all of the models used Deep Learning to extract the features [ 34 , 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

et al. 2022

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Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

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“…Remarkable progress has been made in the automated detection of COVID-19 in CXRs [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Several studies in the literature have leveraged deep convolutional neural networks (CNNs) with and without modifications to convincingly predict COVID-19.…”

Section: Related Workmentioning

confidence: 99%

“…Several multi-classifications of COVID-19 from normal, pneumonia, and TB have been developed using deep CNNs [15][16][17][18][19][20][21]. Wang et al [15] first introduced an opensource COVID-Net to identify COVID-19 CXRs using a customized CNN model.…”

Section: Related Workmentioning

confidence: 99%

“…They achieved the highest accuracy of 99% with the ensemble classifier by using majority voting on the dataset of 237 COVID-19, 1338 normal, and 1336 viral pneumonia CXRs. Shelke et al [21] presented a three-stage COVID-19 detection and severity scoring metric. The first stage used VGG-16 to differentiate between normal, pneumonia and TB, and achieved 95.9% accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

et al. 2021

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The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

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Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening

Cited by 75 publications

References 23 publications

A deep learning-based framework for detecting COVID-19 patients using chest X-rays

A deep learning-based framework for detecting COVID-19 patients using chest X-rays

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

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