COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Singh, Rajeev Kumar; Pandey, Rohan; Babu, Rishie Nandhan

doi:10.1007/s00521-020-05636-6

Cited by 80 publications

(38 citation statements)

References 78 publications

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“…They have evaluated their approach for Glioma Cancer prediction and have shown a comparable performance with black box methods with the added advantage of explainable predictions. Besides, the work reported in [ 50 ] is a recent method, where an explainable deep learning framework has been presented for COVID-19 diagnosis in chest X-rays. The authors have utilized the Grad-CAM approach for obtaining explainability from their CNN base learners.…”

Section: Resultsmentioning

confidence: 99%

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays

Paul

Basu

Mahmud

et al. 2022

Neural Comput & Applic

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Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients’ lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.

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Section: Resultsmentioning

confidence: 99%

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays

Paul

Basu

Mahmud

et al. 2022

Neural Comput & Applic

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“…The dataset included chest X-rays from the public dataset, and the best classification accuracy of 98.97% was obtained by SVM. Since the variance of a single CNN classifier is usually too high, which leads to poor generalization in practical application, Singh et al [107] built a COVIDScreen network. The COVIDScreen was based on the pruning learning algorithm, which solved the problem of generalization and complexity based on multiple CNN learners.…”

Section: Covid-19 Diagnosis Based On Convolutional Neural Network Training From Scratchmentioning

confidence: 99%

A Survey on Machine Learning in COVID-19 Diagnosis

Guo¹,

Zhang²,

Lu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What's more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.

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“…Existing deep learning models lack the explanation for their detection or classification of inputs, so the physicians may not be able to understand why the models have predicted as they did [49,50]. Therefore, the black box nature of the models needs to be improved with explainable AI.…”

Section: Challenges and Conclusionmentioning

confidence: 99%

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

Surianarayanan

Raj

2021

New Gener. Comput.

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The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.

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COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Cited by 80 publications

References 78 publications

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays

A Survey on Machine Learning in COVID-19 Diagnosis

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

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