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  1. Artikel ; Online: S

    Alam, Md Jahin / Mohammad, Mir Sayeed / Hossain, Md Adnan Faisal / Showmik, Ishtiaque Ahmed / Raihan, Munshi Sanowar / Ahmed, Shahed / Mahmud, Talha Ibn

    Computers in biology and medicine

    2022  Band 150, Seite(n) 106148

    Abstract: Dermoscopic images ideally depict pigmentation attributes on the skin surface which is highly regarded in the medical community for detection of skin abnormality, disease or even cancer. The identification of such abnormality, however, requires trained ... ...

    Abstract Dermoscopic images ideally depict pigmentation attributes on the skin surface which is highly regarded in the medical community for detection of skin abnormality, disease or even cancer. The identification of such abnormality, however, requires trained eyes and accurate detection necessitates the process being time-intensive. As such, computerized detection schemes have become quite an essential, especially schemes which adopt deep learning tactics. In this paper, a convolutional deep neural network, S
    Mesh-Begriff(e) Humans ; Dermoscopy/methods ; Skin Neoplasms/diagnostic imaging ; Neural Networks, Computer ; Skin/diagnostic imaging ; Image Processing, Computer-Assisted/methods
    Sprache Englisch
    Erscheinungsdatum 2022-09-28
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.106148
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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

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

    Biocybernetics and biomedical engineering

    2021  Band 41, Heft 4, Seite(n) 1685–1701

    Abstract: 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, ...

    Abstract 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.
    Sprache Englisch
    Erscheinungsdatum 2021-10-20
    Erscheinungsland Poland
    Dokumenttyp Journal Article
    ZDB-ID 605560-6
    ISSN 0208-5216
    ISSN 0208-5216
    DOI 10.1016/j.bbe.2021.09.004
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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