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  1. Article ; 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.
    Keywords Coronavirus ; COVID-19 ; Deep learning ; Chest X-ray ; Convolutional neural network ; Long short-term memory ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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

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

    Informatics in Medicine Unlocked

    2020  Volume 20, Page(s) 100412

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ISSN 2352-9148
    DOI 10.1016/j.imu.2020.100412
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images.

    Islam, Md Zabirul / Islam, Md Milon / Asraf, Amanullah

    Informatics in medicine unlocked

    2020  Volume 20, Page(s) 100412

    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.
    Keywords covid19
    Language English
    Publishing date 2020-08-15
    Publishing country England
    Document type Journal Article
    ISSN 2352-9148
    ISSN 2352-9148
    DOI 10.1016/j.imu.2020.100412
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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    Inter-library loan at ZB MED

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  4. Article ; Online: Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

    Asraf, Amanullah / Islam, Md Zabirul / Haque, Md Rezwanul / Islam, Md Milon

    SN computer science

    2020  Volume 1, Issue 6, Page(s) 363

    Abstract: During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a ... ...

    Abstract During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.
    Keywords covid19
    Language English
    Publishing date 2020-11-03
    Publishing country Singapore
    Document type Journal Article ; Review
    ISSN 2661-8907
    ISSN (online) 2661-8907
    DOI 10.1007/s42979-020-00383-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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  5. Article ; 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.
    Keywords COVID-19 ; Deep transfer learning ; Chest X-rays ; Recurrent neural network ; Science ; Q ; Engineering (General). Civil engineering (General) ; TA1-2040
    Subject code 006
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher KeAi Communications Co. Ltd.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning

    Islam, Md. Milon / Islam, Md. Zabirul / Asraf, Amanullah / Ding, Weiping

    medRxiv

    Abstract: The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse- ... ...

    Abstract The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available 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 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy and computational time in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. 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.
    Keywords covid19
    Language English
    Publishing date 2020-08-31
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.08.24.20181339
    Database COVID19

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