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

Paul, Ashis Kumar; Basu, Arpan; Mahmud, Mufti; Kaiser, M. Shamim; Sarkar, Ram

doi:10.1007/s00521-021-06737-6

Cited by 60 publications

(24 citation statements)

References 45 publications

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“…Further, Paul et al, (2022) created a deep learning model and many transfer learning models were put together to classify CXR images simultaneously. Two databases of CXR images were considered for this research.…”

Section: Diagnosis Of Covid-19 Using Chest X-raysmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

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In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

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Section: Diagnosis Of Covid-19 Using Chest X-raysmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

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“…In this section, the performance of the present Sugeno fuzzy integral aided ensemble method is compared with several state-of-the-art methods proposed by Makris, Kontopoulos, and Tserpes (2020) , Horry, et al (2020) , Hemdan et al (2020) , Ardakani et al (2020) , Aslan et al (2021) , Bashar et al (2021) , Chowdhury, et al (2020) , Das, Roy, et al (2021) , Goel et al (2021) , Islam et al (2020) , Ismael and Şengür (2021) , Jain et al (2021) , Kedia et al (2021) , Khan et al (2020) , Mukherjee et al (2021a) , Naeem and Bin-Salem (2021) , Panetta et al (2021) , Paul et al (2022) , Roy et al (2021) , Sedik, Hammad, Abd El-Samie, Gupta, and Abd El-Latif (2021) , Senan et al (2021) , and Yang, et al (2021) on all three datasets used here whenever applicable i.e., the results obtained by the methods on the respective datasets are cited or in few cases, their performances are evaluated using the proposed setup. The comparative results are recorded in Table 2 , Table 3 , Table 4 for dataset 1, 2 and 3 (mentioned in Table 1 ) respectively.…”

Section: Resultsmentioning

confidence: 99%

“… Work ref. Model used Performance (in %) in terms of Precision Recall F1-score Accuracy Aslan et al (2021) mAlexNet+BiLSTM 98.77 98.76 98.76 98.70 Aslan et al (2021) mAlexNet 98.16 98.26 98.20 98.14 Kedia et al (2021) CoVNet-19 98.34 98.34 98.34 98.20 Sedik et al (2021) ConvLSTM 94.67 97.09 95.64 95.96 Panetta et al (2021) Classical Fibonacci p-pattern 97.78 96.90 97.32 97.79 Panetta et al (2021) Fibonacci p-pattern 97.20 96.76 96.69 98.03 Yang, et al (2021) Fast.AI ResNet 97.00 97.00 97.00 97.00 Paul et al (2022) a Inverted Bell Ensemble 97.24 97.25 97.24 …”

Section: Resultsmentioning

confidence: 99%

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CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays

Dey¹,

Bhattacharya²,

Malakar³

et al. 2022

Expert Systems with Applications

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“…On the validation set, the suggested model was found to have an accuracy of 97.60% and a sensitivity of 92.90%. To detect COVID-19 from chest radiograph images, the authors developed an inverted bell-curve-based ensemble of DL frameworks in [12]. For this purpose, the pre-trained models were rst retrained with radiograph datasets using a TL method and integrated with the suggested inverted bell curve weighted ensemble approach, which assigns a weight to each classi er's output and performs a weighted average of those outputs to get the nal prediction.…”

Section: Three-class Classi Cation Using Chest Radiographs Comparison...mentioning

confidence: 99%

An Effective Approach for Automatic COVID-19 Detection from Multiple Image Sources Using Shufflenet Convolutional Neural Network (CNN)

Ullah

Raza

Khan

et al. 2022

Preprint

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Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques such as Chest X-ray or chest radiographs, Computed Tomography (CT) scan, and electrocardiogram (ECG) trace images are most widely known for early discovery and analysis of the Coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i-e, binary, three-class, and four-class classifications. Also, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i-e, binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 radiography database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms the state-of-the-art DL approaches such as Squeezenet, Alexnet, and Darknet19 by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

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

Cited by 60 publications

References 45 publications

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays

An Effective Approach for Automatic COVID-19 Detection from Multiple Image Sources Using Shufflenet Convolutional Neural Network (CNN)

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