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  1. Article ; Online: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

    Jaiswal, Aayush / Gianchandani, Neha / Singh, Dilbag / Kumar, Vijay / Kaur, Manjit

    Journal of biomolecular structure & dynamics

    2020  Volume 39, Issue 15, Page(s) 5682–5689

    Abstract: ... transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or ... architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep ... reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches ...

    Abstract Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.
    MeSH term(s) COVID-19 ; Deep Learning ; Humans ; Neural Networks, Computer ; SARS-CoV-2 ; Tomography, X-Ray Computed
    Keywords covid19
    Language English
    Publishing date 2020-07-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 49157-3
    ISSN 1538-0254 ; 0739-1102
    ISSN (online) 1538-0254
    ISSN 0739-1102
    DOI 10.1080/07391102.2020.1788642
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  2. Article ; Online: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

    Jaiswal, Aayush / Gianchandani, Neha / Singh, Dilbag / Kumar, Vijay / Kaur, Manjit

    Journal of Biomolecular Structure and Dynamics

    2020  , Page(s) 1–8

    Keywords Molecular Biology ; Structural Biology ; General Medicine ; covid19
    Language English
    Publisher Informa UK Limited
    Publishing country uk
    Document type Article ; Online
    ZDB-ID 49157-3
    ISSN 1538-0254 ; 0739-1102
    ISSN (online) 1538-0254
    ISSN 0739-1102
    DOI 10.1080/07391102.2020.1788642
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2093: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

    Jaiswal, Aayush / Gianchandani, Neha / Singh, Dilbag / Kumar, Vijay / Kaur, Manjit

    J Biomol Struct Dyn

    Abstract: ... transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or ... architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep ... reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches ...

    Abstract Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #629507
    Database COVID19

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  4. Article: Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss.

    Chamseddine, Ekram / Mansouri, Nesrine / Soui, Makram / Abed, Mourad

    Applied soft computing

    2022  Volume 129, Page(s) 109588

    Abstract: ... on deep learning (DL) techniques have shown high performance in classifying COVID-19 chest X-ray images ... transfer learning (TL) on three different COVID-19 datasets. The models were developed to perform a multi ... we aim to build an accurate model that assists clinicians in the early diagnosis of COVID-19 using ...

    Abstract Healthcare systems worldwide have been struggling since the beginning of the COVID-19 pandemic. The early diagnosis of this unprecedented infection has become their ultimate objective. Detecting positive patients from chest X-ray images is a quick and efficient solution for overloaded hospitals. Many studies based on deep learning (DL) techniques have shown high performance in classifying COVID-19 chest X-ray images. However, most of these studies suffer from a class imbalance problem mainly due to the limited number of COVID-19 samples. Such a problem may significantly reduce the efficiency of DL classifiers. In this work, we aim to build an accurate model that assists clinicians in the early diagnosis of COVID-19 using balanced data. To this end, we trained six state-of-the-art convolutional neural networks (CNNs) via transfer learning (TL) on three different COVID-19 datasets. The models were developed to perform a multi-classification task that distinguishes between COVID-19, normal, and viral pneumonia cases. To address the class imbalance issue, we first investigated the Weighted Categorical Loss (WCL) and then the Synthetic Minority Oversampling Technique (SMOTE) on each dataset separately. After a comparative study of the obtained results, we selected the model that achieved high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and AUC compared to other recent works. DenseNet201 and VGG-19 claimed the best scores. With an accuracy of 98.87%, an F1_Score of 98.21%, a sensitivity of 98.86%, a specificity of 99.43%, a precision of 100%, and an AUC of 99.15%, the WCL combined with CheXNet outperformed the other examined models.
    Language English
    Publishing date 2022-08-29
    Publishing country United States
    Document type Journal Article
    ISSN 1568-4946
    ISSN 1568-4946
    DOI 10.1016/j.asoc.2022.109588
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  5. Article: Deep transfer learning based classification model for covid-19 using chest CT-scans.

    Lahsaini, Ilyas / El Habib Daho, Mostafa / Chikh, Mohamed Amine

    Pattern recognition letters

    2021  Volume 152, Page(s) 122–128

    Abstract: ... can be beneficial for diagnosing and following up patients with COVID-19. ... in Algeria. Then we performed a transfer learning on deep learning models that got the best results ... on the DenseNet201 architecture and the GradCam explanation algorithm to detect COVID-19 in chest CT images and ...

    Abstract COVID-19 is an infectious and contagious virus. As of this writing, more than 160 million people have been infected since its emergence, including more than 125,000 in Algeria. In this work, We first collected a dataset of 4986 COVID and non-COVID images confirmed by RT-PCR tests at Tlemcen hospital in Algeria. Then we performed a transfer learning on deep learning models that got the best results on the ImageNet dataset, such as DenseNet121, DenseNet201, VGG16, VGG19, Inception Resnet-V2, and Xception, in order to conduct a comparative study. Therefore, We have proposed an explainable model based on the DenseNet201 architecture and the GradCam explanation algorithm to detect COVID-19 in chest CT images and explain the output decision. Experiments have shown promising results and proven that the introduced model can be beneficial for diagnosing and following up patients with COVID-19.
    Language English
    Publishing date 2021-09-22
    Publishing country Netherlands
    Document type Journal Article
    ISSN 0167-8655
    ISSN 0167-8655
    DOI 10.1016/j.patrec.2021.08.035
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