LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 9 of total 9

Search options

  1. Article: The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection.

    Ismael, Aras M / Şengür, Abdulkadir

    Health information science and systems

    2020  Volume 8, Issue 1, Page(s) 29

    Abstract: COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography ( ...

    Abstract COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.
    Keywords covid19
    Language English
    Publishing date 2020-09-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-020-00116-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    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.

  2. Article: Deep learning approaches for COVID-19 detection based on chest X-ray images.

    Ismael, Aras M / Şengür, Abdulkadir

    Expert systems with applications

    2020  Volume 164, Page(s) 114054

    Abstract: COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring ... ...

    Abstract COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
    Keywords covid19
    Language English
    Publishing date 2020-09-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2017237-0
    ISSN 0957-4174
    ISSN 0957-4174
    DOI 10.1016/j.eswa.2020.114054
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    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.

  3. Article ; Online: The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

    Ismael, Aras M. / Şengür, Abdulkadir

    Health Information Science and Systems

    2020  Volume 8, Issue 1

    Keywords covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-020-00116-6
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    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.

  4. Article: Deep Learning Approaches for COVID-19 Detection Based on Chest X-ray Images

    Ismael, Aras M / Sengür, Abdulkadir

    Expert Syst Appl

    Abstract: COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring ... ...

    Abstract COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #799363
    Database COVID19

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article: Two-stepped majority voting for efficient EEG-based emotion classification.

    Ismael, Aras M / Alçin, Ömer F / Abdalla, Karmand Hussein / Şengür, Abdulkadir

    Brain informatics

    2020  Volume 7, Issue 1, Page(s) 9

    Abstract: In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches ... ...

    Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.
    Language English
    Publishing date 2020-09-17
    Publishing country Germany
    Document type Journal Article
    ISSN 2198-4018
    ISSN 2198-4018
    DOI 10.1186/s40708-020-00111-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    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.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article: COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.

    Rahman, Tawsifur / Akinbi, Alex / Chowdhury, Muhammad E H / Rashid, Tarik A / Şengür, Abdulkadir / Khandakar, Amith / Islam, Khandaker Reajul / Ismael, Aras M

    Health information science and systems

    2022  Volume 10, Issue 1, Page(s) 1

    Abstract: The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been ... ...

    Abstract The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.
    Language English
    Publishing date 2022-01-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-021-00169-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    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.

  7. Article ; Online: Two-stepped majority voting for efficient EEG-based emotion classification

    Aras M. Ismael / Ömer F. Alçin / Karmand Hussein Abdalla / Abdulkadir Şengür

    Brain Informatics, Vol 7, Iss 1, Pp 1-

    2020  Volume 12

    Abstract: Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based ... ...

    Abstract Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.
    Keywords EEG-based emotion recognition ; EEG rhythms ; Wavelet packet entropies ; Fractal dimensions ; Majority voting ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Computer software ; QA76.75-76.765
    Subject code 006
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Book ; Online: COV-ECGNET

    Rahman, Tawsifur / Akinbi, Alex / Chowdhury, Muhammad E. H. / Rashid, Tarik A. / Şengür, Abdulkadir / Khandakar, Amith / Islam, Khandaker Reajul / Ismael, Aras M.

    COVID-19 detection using ECG trace images with deep convolutional neural network

    2021  

    Abstract: The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been ... ...

    Abstract The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consists of 1937 images from five distinct categories, such as Normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

    Comment: 24 pages
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-06-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Article ; Online: Deep learning approaches for COVID-19 detection based on chest X-ray images

    Ismael, Aras M. / Şengür, Abdulkadir

    Expert Systems with Applications

    Volume 164, Page(s) 114054

    Keywords General Engineering ; Artificial Intelligence ; Computer Science Applications ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2017237-0
    ISSN 0957-4174
    ISSN 0957-4174
    DOI 10.1016/j.eswa.2020.114054
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    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.

To top