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  1. Article ; Online: Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework

    Sakib, Shadman / Siddique, Md. Abu Bakr / Khan, Mohammad Mahmudur Rahman / Yasmin, Nowrin / Aziz, Anas / Chowdhury, Madiha / Tasawar, Ihtyaz Kader

    medRxiv

    Abstract: The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is ...

    Abstract The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, the implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00, and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.
    Keywords covid19
    Language English
    Publishing date 2020-11-12
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.11.08.20227819
    Database COVID19

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  2. Book ; Online: Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

    Siddique, Md. Abu Bakr / Sakib, Shadman / Khan, Mohammad Mahmudur Rahman / Tanzeem, Abyaz Kader / Chowdhury, Madiha / Yasmin, Nowrin

    2020  

    Abstract: Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years, primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved ... ...

    Abstract Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years, primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved at the cost of immense time and effort and demands a lot of expertise from engaged personnel. This substantiates the necessity of fabricating an autonomous model brain tumor diagnosis. Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. The dataset used in this paper consists of 253 brain MR images where 155 images are reported to have tumors. Our model can single out the MR images with tumors with an overall accuracy of 96%. The model outperformed the existing conventional methods for the diagnosis of brain tumor in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score = 0.97). Moreover, the proposed model's average precision-recall score is 0.93, Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can help clinical experts verify whether the patient has a brain tumor and, consequently, accelerate the treatment procedure.

    Comment: 4th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDIA
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2020-10-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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