Computer-Aided COVID-19 Screening from Chest CT-Scan using a Fuzzy Ensemble-based Technique

Sahoo, Pranab; Saha, Sriparna; Mondal, Samrat; Chowdhury, Sujit; Gowda, Suraj

doi:10.1109/ijcnn55064.2022.9892085

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Deep bidirectional classification models have been used to detect COVID-19 from CT scans, achieving an accuracy of 96.19% [30]. Fuzzy ensemble-based CNNs were able to achieve an accuracy of 98% on publicly available CT-Scans [31]. Deep CT-Net, a pixel-wise attention model, achieved an accuracy of 81% and an area under the curve of 92% [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

El Lel,

Ahsan,

Haider

2023

Computers

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Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

El Lel,

Ahsan,

Haider

2023

Computers

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“…They utilized a fuzzy distancebased ensemble technique multiplying the distances associated with each class and selecting the minimum value as the final output. Several other authors [29], [30], [24], [31] also used ensemble techniques in different medical image analysis problems and reported better performance. In recent studies, researchers have explored several simple fusion approaches to combine the outputs of different models and enhance overall performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Computer-Aided Cervical Cancer Detection Using a Novel Fuzzy Rank-Based Fusion

Sahoo,

Saha,

Mondal

et al. 2023

IEEE Access

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Cervical cancer is a severe and pervasive disease that poses a significant health threat to women globally. The Pap smear test is an efficient and effective method for detecting cervical cancer in its early stages. However, manual screening is labor-intensive and requires expert cytologists, leading to potential delays and inconsistencies in diagnosis. Deep Learning-based Computer-Aided Diagnosis (CAD) has shown significant results and can ease the problem of manual screening. However, one single model is sometimes insufficient to capture the complex data pattern for accurate disease prediction. In this work, we develop an end-to-end architecture utilizing three pre-trained models for the initial cervical cancer prediction. To aggregate the outcomes of these models, we propose a novel fuzzy rank-based ensemble considering two non-linear functions for the final level prediction. Unlike simple fusion techniques, the proposed architecture provides the final predictions on the test samples by considering the base classifier's confidence in the predictions. To further enhance the classification capabilities of these models, we integrate advanced augmentation techniques such as CutOut, MixUp, and CutMix. The proposed model is evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, using a 5-fold cross-validation approach. On the SIPaKMeD dataset, the proposed ensemble architecture achieves a classification accuracy of 97.18% and an F1-score of 97.16%. On the Mendeley LBC dataset, the accuracy reaches 99.22% with an F1-score of 99.19%. Experimental results demonstrate the proposed architecture's effectiveness and potential in cervical Pap smear image classification. This could aid medical professionals in making more informed treatment decisions, improving overall effectiveness in the testing process.

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Vision Transformer-Based Federated Learning for COVID-19 Detection Using Chest X-Ray

Sahoo

Saha

Mondal

et al. 2023

Communications in Computer and Information Science

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Computer-Aided COVID-19 Screening from Chest CT-Scan using a Fuzzy Ensemble-based Technique

Cited by 7 publications

References 21 publications

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Enhancing Computer-Aided Cervical Cancer Detection Using a Novel Fuzzy Rank-Based Fusion

Vision Transformer-Based Federated Learning for COVID-19 Detection Using Chest X-Ray

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