A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images

Bhattacharyya, Abhijit; Bhaik, Divyanshu; Kumar, Sunil; Thakur, Prayas; Sharma, Rahul; Pachori, Ram Bilas

doi:10.1016/j.bspc.2021.103182

Cited by 130 publications

(62 citation statements)

References 34 publications

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“…[ 1 ]. The infection causes pulmonary complications, such as severe pneumonia, which leads to death in several cases [ 1 , 2 ]. The possible containment of this virus requires quick detection to ensure that the exposed person can be isolated as quickly as possible to mitigate the spread of the disease because it is highly contagious.…”

Section: Introductionmentioning

confidence: 99%

“…Reverse transcription-polymerase chain reaction (RT-PCR) [ 1 ] is the standard method used to detect COVID-19; in this method, sputum or nasopharyngeal swab is tested for the presence of the viral RNA. RT-PCR has the following limitations: the long time required to produce results, limited or lack of test materials in medical centers and hospitals [ 2 ], and relatively low sensitivity; this condition is not conducive to the main objective of detection of rapid positive samples for immediate isolation [ 3 ]. The utilization of medical images, such as chest X-ray images or computed tomography (CT) scans, can be an alternative solution for fast screening [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, physicians are trying hard to contain this disease. Thus, artificial intelligence- (AI-) based decision support tools should be developed to identify and use the image at the lung level in segmenting the infection [ 2 ]. AI, such as machine and deep neural network (DNN) techniques [ 5 ], has been increasingly used in recent years as the main tool to find solutions to diverse difficulties, such as object detection [ 6 – 8 ], image classification [ 9 ], and speech recognition [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

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Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

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“…The authors applied eight different pre-trained neural networks using the transfer learning method and obtained a maximum accuracy of 98.33% using ResNet34 network. When it comes to fusion of machine learning and deep learning, Bhattacharya et al [101] performed three-class classification. This was aimed at classification of CXRs into COVID-19, pneumonia, and normal class.…”

Section: Related Workmentioning

confidence: 99%

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

et al. 2022

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Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

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“…Such data sets include information on COVID-19 diagnosis, case reporting, transmission estimation, sentiment analysis from social media, and semantic analysis from the collection of scholarly articles. Most of the COVID-19 diagnosis data are based on image (CT scans, X-ray images) analysis [20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

et al. 2021

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New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.

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A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images

Cited by 130 publications

References 34 publications

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

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