AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images

• Autorzy: Rashid Nayeeb , Hossain Md Adnan Faisal , Ali Mohammad , Islam Sukanya Mumtahina , Mahmud Tanvir , Fattah Shaikh Anowarul

Identyfikatory

DOI

10.1016/j.bbe.2021.09.004

• Języki publikacji: EN

Abstrakty

EN

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

Słowa kluczowe

EN

COVID-19 diagnosis; medical image analysis; X-ray image; neural network; autoencoder

PL

diagnoza COVID-19; analiza obrazu medycznego; obrazowanie rentgenowskie; sieć neuronowa; autoenkoder

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1685--1701
• Opis fizyczny: Bibliogr, 44 poz., rys., tab.

Twórcy

autor

Rashid Nayeeb

• Department of EEE, BUET, Dhaka, Bangladesh

autor

Hossain Md Adnan Faisal

• Department of EEE, BUET, Dhaka, Bangladesh

autor

Ali Mohammad

• Department of EEE, BUET, Dhaka, Bangladesh

autor

Islam Sukanya Mumtahina

• Department of EEE, BUET, Dhaka, Bangladesh

autor

Mahmud Tanvir

• Department of EEE, BUET, Dhaka, Bangladesh

autor

Fattah Shaikh Anowarul

• Department of EEE, BUET, ECE Building, West Palashi, Dhaka 1205, Bangladesh

Bibliografia

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