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  1. AU="Md. Zabirul Islam" AU="Md. Zabirul Islam"
  2. AU="Butcher, Xochitl"
  3. AU="Espay, Alberto J."

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  1. Artikel ; Online: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

    Md. Zabirul Islam / Md. Milon Islam / Amanullah Asraf

    Informatics in Medicine Unlocked, Vol 20, Iss , Pp 100412- (2020)

    2020  

    Abstract: Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and ... ...

    Abstract Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
    Schlagwörter Coronavirus ; COVID-19 ; Deep learning ; Chest X-ray ; Convolutional neural network ; Long short-term memory ; Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2020-01-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

    Md. Milon Islam / Md. Zabirul Islam / Amanullah Asraf / Mabrook S. Al-Rakhami / Weiping Ding / Ali Hassan Sodhro

    BenchCouncil Transactions on Benchmarks, Standards and Evaluations, Vol 2, Iss 4, Pp 100088- (2022)

    2022  

    Abstract: Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques ...

    Abstract Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff.All the data used during the study are openly available from the Mendeley data repository at https://data.mendeley.com/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://github.com/Asraf047/COVID19-CNN-RNN.
    Schlagwörter COVID-19 ; Deep transfer learning ; Chest X-rays ; Recurrent neural network ; Science ; Q ; Engineering (General). Civil engineering (General) ; TA1-2040
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2022-10-01T00:00:00Z
    Verlag KeAi Communications Co. Ltd.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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