EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

Addo, Daniel; Zhou, Shunhua; Jackson, Jehoiada; Nneji, Grace Ugochi; Monday, Happy Nkanta; Sarpong, Kwabena; Patamia, Rutherford Agbeshi; Ekong, Favour; Owusu-Agyei, Christyn Akosua

doi:10.3390/diagnostics12112569

Cited by 10 publications

(2 citation statements)

References 86 publications

(103 reference statements)

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“…While recall, precision, and F1-score of 0.48, 0.44, and 0.45 were achieved on Shenzhen datasets. Addo et al [72] performed an extensive experiment to diagnose Covid-19 from X-ray datasets using EoAE. The method performed the diagnosis in two steps by proposing three different variational autoencoder models.…”

Section: B X-raymentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an aberration from normal behavior, or an unexpected observation requiring immediate attention. The detection of anomalies in biomedical data has a direct impact on the health and safety of individuals. However, anomalous events are rare, diverse, and infrequent. Often, the collection of anomalous data may involve significant loss of human life and healthcare costs. Therefore, traditional supervised machine and deep learning algorithms may not be directly applicable to such problems. Biomedical data are often collected in the form of images, electronic health records, and time series. Typically, an autoencoder (AE) or its corresponding variant is trained on normal data, and an anomaly is identified as a significant deviation from these data based on reconstruction error or other metrics. An Ensemble of AEs (EoAEs) can serve as a robust approach for anomaly detection in biomedical data by combining diverse and accurate views of normal data. An EoAE can provide superior detection to a single encoder; however, its performance can depend on various factors, including the diversity of the created data, the accuracy of the individual AEs, and the combination of their outcomes. Herein, we perform a comprehensive narrative literature review on the use of EoAEs when using different types of biomedical data. Such an ensemble provides a promising approach for anomaly detection in biomedical data, offering the potential for performance improvement by leveraging the strengths of diverse AEs. However, several challenges remain, such as the need for data specification and determination of the optimal number of AEs in the ensemble. By addressing these challenges, researchers can enhance the effectiveness of EoAEs for anomaly detection in various types of biomedical data. Furthermore, through this review, we highlight the significance of evaluating and comparing the performance of an EoAE with that of single AEs by establishing agreed-upon evaluation metrics and investigating normalization techniques for anomaly scores. We conclude the review by presenting challenges and open questions in the field with for future research.

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Section: B X-raymentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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“…In their study [38], they proposed three effective EVAE-Net models for the detection of COVID-19. They trained two encoders on the images of chest X-rays to generate two feature maps.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder

Guleria

Luqmani

Kothari

et al. 2023

IJERPH

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A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.

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Bibliography

2024

Deep Learning

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EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

Cited by 10 publications

References 86 publications

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder

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