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  1. Article: Design ensemble deep learning model for pneumonia disease classification.

    El Asnaoui, Khalid

    International journal of multimedia information retrieval

    2021  Volume 10, Issue 1, Page(s) 55–68

    Abstract: With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic ... ...

    Abstract With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
    Language English
    Publishing date 2021-02-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2649485-1
    ISSN 2192-662X ; 2192-6611
    ISSN (online) 2192-662X
    ISSN 2192-6611
    DOI 10.1007/s13735-021-00204-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Using X-ray images and deep learning for automated detection of coronavirus disease.

    El Asnaoui, Khalid / Chawki, Youness

    Journal of biomolecular structure & dynamics

    2020  Volume 39, Issue 10, Page(s) 3615–3626

    Abstract: Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and ... ...

    Abstract Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.
    MeSH term(s) COVID-19/diagnostic imaging ; Deep Learning ; Diagnosis, Computer-Assisted ; Humans ; Image Interpretation, Computer-Assisted ; X-Rays
    Keywords covid19
    Language English
    Publishing date 2020-05-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 49157-3
    ISSN 1538-0254 ; 0739-1102
    ISSN (online) 1538-0254
    ISSN 0739-1102
    DOI 10.1080/07391102.2020.1767212
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Image Compression Based on Block SVD Power Method

    Asnaoui Khalid El

    Journal of Intelligent Systems, Vol 29, Iss 1, Pp 1345-

    2019  Volume 1359

    Abstract: In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more ... ...

    Abstract In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more significant for reducing data redundancy in order to allow more transfer and storage of data. In this context, this paper addresses the problem of lossy image compression. Indeed, this new proposed method is based on the block singular value decomposition (SVD) power method that overcomes the disadvantages of MATLAB’s SVD function in order to make a lossy image compression. The experimental results show that the proposed algorithm has better compression performance compared with the existing compression algorithms that use MATLAB’s SVD function. In addition, the proposed approach is simple in terms of implementation and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.
    Keywords image compression ; singular value decomposition ; block svd power method ; lossy image compression ; psnr ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 518
    Language English
    Publishing date 2019-04-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Using X-ray images and deep learning for automated detection of coronavirus disease

    El Asnaoui, Khalid / Chawki, Youness

    J Biomol Struct Dyn

    Abstract: Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and ... ...

    Abstract Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #245625
    Database COVID19

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  5. Article ; Online: Automatically Assess Day Similarity Using Visual Lifelogs

    Asnaoui Khalid El / Radeva Petia

    Journal of Intelligent Systems, Vol 29, Iss 1, Pp 298-

    2018  Volume 310

    Abstract: Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of ... ...

    Abstract Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results.
    Keywords lifelogging ; day similarity ; similarity measure ; edub ; dtw ; 86u10 ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 004
    Language English
    Publishing date 2018-02-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

    Khalid Asnaoui El / Youness Chawki / Ali Idri

    Abstract: Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia ... ...

    Abstract Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray&CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).
    Keywords covid19
    Publisher arxiv
    Document type Article
    Database COVID19

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  7. Article: Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

    Asnaoui, Khalid El / Chawki, Youness / Idri, Ali

    Abstract: Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia ... ...

    Abstract Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray&CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  8. Book ; Online: Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

    Asnaoui, Khalid El / Chawki, Youness / Idri, Ali

    2020  

    Abstract: Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia ... ...

    Abstract Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; covid19
    Subject code 006
    Publishing date 2020-03-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: A content based image retrieval method based on k-means clustering technique

    Ouhda, Mohamed / Aksasse, Brahim / El Asnaoui, Khalid / Ouanan, Mohammed

    Journal of electronic commerce in organizations : the international journal of electronic commerce in modern organizations ; an official publication of the Information Resources Management Association Vol. 16, No. 1 , p. 82-96

    2018  Volume 16, Issue 1, Page(s) 82–96

    Author's details Mohamed Ouhda, Khalid El Asnaoui, Mohammed Ouanan, Brahim Aksasse
    Keywords CBIR ; Classification ; K-Means ; Segmentation ; Similarity Measure
    Language English
    Dates of publication 2018-9999
    Publisher IGI Global
    Publishing place Hershey, Pa.
    Document type Article
    ZDB-ID 2270169-2
    ISSN 1539-2937
    Database ECONomics Information System

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