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Unsupervised Deep learning-based Feature Fusion Approach for Detection and Analysis of COVID-19 using X-ray and CT Images
Open Bioinformatics Journal ; 15, 2022.
Article in English | Scopus | ID: covidwho-2054703
ABSTRACT

Aims:

This study investigates an unsupervised deep learning-based feature fusion approach for the detection and analysis of COVID-19 using chest X-ray (CXR) and Computed tomography (CT) images.

Background:

The outbreak of COVID-19 has affected millions of people all around the world and the disease is diagnosed by the reverse transcription-polymerase chain reaction (RT-PCR) test which suffers from a lower viral load, and sampling error, etc. Computed tomography (CT) and chest X-ray (CXR) scans can be examined as most infected people suffer from lungs infection. Both CT and CXR imaging techniques are useful for the COVID-19 diagnosis at an early stage and it is an alternative to the RT-PCR test.

Objective:

The manual diagnosis of CT scans and CXR images are labour-intensive and consumes a lot of time. To handle this situation, many AI-based solutions are researched including deep learning-based detection models, which can be used to help the radiologist to make a better diagnosis. However, the availability of annotated data for COVID-19 detection is limited due to the need for domain expertise and expensive annotation cost. Also, most existing state-of-the-art deep learning-based detection models follow a supervised learning approach. Therefore, in this work, we have explored various unsupervised learning models for COVID-19 detection which does not need a labelled dataset.

Methods:

In this work, we propose an unsupervised deep learning-based COVID-19 detection approach that incorporates the feature fusion method for performance enhancement. Four different sets of experiments are run on both CT and CXR scan datasets where convolutional autoencoders, pre-trained CNNs, hybrid, and PCA-based models are used for feature extraction and K-means and GMM techniques are used for clustering.

Results:

The maximum accuracy of 84% is achieved by the model Autoencoder3-ResNet50 (GMM) on the CT dataset and for the CXR dataset, both Autoencoder1-VGG16 (KMeans and GMM) models achieved 70% accuracy.

Conclusion:

Our proposed deep unsupervised learning, feature fusion-based COVID-19 detection approach achieved promising results on both datasets. It also outperforms four well-known existing unsupervised approaches. © 2022 Ravi and Pham.
Keywords

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Open Bioinformatics Journal Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Open Bioinformatics Journal Year: 2022 Document Type: Article