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Chest X-Ray Analysis of Tuberculosis by Convolutional Neural Networks with Affine Transforms

Published: 08 December 2018 Publication History

Abstract

Applying deep learning techniques for classification of medical images has seen considerable growth in recent years. Among several, Convolutional Neural Net-works (CNNs) are a class of powerful models well known for image classification and segmentation. This research introduces the concept of computer-aided diagnosis that could help in early diagnosis of Tuberculosis infection. The three CNN architectures: AlexNet, VGG-16 and CapsNet, were customized to classify tuberculosis lesions in CXR images. Data augmentation with rotating was used to mimic the real world as CXR images may not be precisely vertical. The performance of the three classifiers was evaluated with the measures: accuracy, sensitivity and specificity. The result showed that CapsNet outperformed the other models when predicting affined images.

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Cited By

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  • (2023)Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature ReviewJournal of Medical Internet Research10.2196/4315425(e43154)Online publication date: 3-Jul-2023
  • (2022)A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest RadiographFrontiers in Medicine10.3389/fmed.2022.8305159Online publication date: 10-Mar-2022
  • (2022)Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) RadiographyIEEE Access10.1109/ACCESS.2022.319415210(85442-85458)Online publication date: 2022
  • Show More Cited By

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  1. Chest X-Ray Analysis of Tuberculosis by Convolutional Neural Networks with Affine Transforms

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    CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence
    December 2018
    641 pages
    ISBN:9781450366069
    DOI:10.1145/3297156
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Shenzhen University: Shenzhen University

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    New York, NY, United States

    Publication History

    Published: 08 December 2018

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    Author Tags

    1. Affine Transforms
    2. Computer-Aided Diagnosis
    3. Convolutional Neural Network
    4. Tuberculosis

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    Cited By

    View all
    • (2023)Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature ReviewJournal of Medical Internet Research10.2196/4315425(e43154)Online publication date: 3-Jul-2023
    • (2022)A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest RadiographFrontiers in Medicine10.3389/fmed.2022.8305159Online publication date: 10-Mar-2022
    • (2022)Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) RadiographyIEEE Access10.1109/ACCESS.2022.319415210(85442-85458)Online publication date: 2022
    • (2022)An Overview of Pulmonary Tuberculosis Detection and Classification Using Machine Learning and Deep Learning AlgorithmsProceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences10.1007/978-981-16-5747-4_72(839-859)Online publication date: 1-Jan-2022
    • (2022)A Review of Capsule Networks in Medical Image AnalysisArtificial Neural Networks in Pattern Recognition10.1007/978-3-031-20650-4_6(65-80)Online publication date: 11-Nov-2022
    • (2021)Ensemble of EfficientNets for the Diagnosis of TuberculosisComputational Intelligence and Neuroscience10.1155/2021/97908942021Online publication date: 14-Dec-2021
    • (2021)Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptorsPhysical and Engineering Sciences in Medicine10.1007/s13246-020-00966-0Online publication date: 18-Jan-2021
    • (2021)Ensemble of Convolution Neural Networks for Automatic Tuberculosis ClassificationComputational Collective Intelligence10.1007/978-3-030-88081-1_41(549-559)Online publication date: 30-Sep-2021

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