Proceedings ArticleDOI
Deep-learning: A potential method for tuberculosis detection using chest radiography
Rahul Hooda,Sanjeev Sofat,Simranpreet Kaur,Ajay Mittal,Fabrice Meriaudeau +4 more
- pp 497-502
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TLDR
A potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal, which is presented in this paper.Abstract:
Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.read more
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Journal ArticleDOI
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
Tawsifur Rahman,Amith Khandakar,Muhammad Abdul Kadir,Khandaker Rejaul Islam,Khandakar F. Islam,Rashid Mazhar,Tahir Hamid,Mohammad Tariqul Islam,Saad Bin Abul Kashem,Zaid Bin Mahbub,Mohamed Arselene Ayari,Muhammad E. H. Chowdhury +11 more
TL;DR: This work has detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques and confirmed that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy.
Journal ArticleDOI
Image Enhancement for Tuberculosis Detection Using Deep Learning
TL;DR: This work assesses the effect of image enhancement on performance of DL technique to address the low contrast of TB chest x-ray (CXR) images, which often is in poor quality.
Journal ArticleDOI
Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition
TL;DR: Deep learning refers to Convolutional Neural Network (CNN) used for image recognition for this study and the dataset is named Fruits-360 and it is obtained from the Kaggle dataset.
Proceedings ArticleDOI
Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks.
Arata Andrade Saraiva,D. B. S. Santos,Nator Junior C. Costa,Jose Vigno Moura Sousa,Nuno M. F. Ferreira,António Valente,Salviano Soares +6 more
TL;DR: A comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia, using the Chest-X-Ray data set provided by Kermany et al., 2018.
Book ChapterDOI
Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography
TL;DR: This study examines the efficiency of deep convolutional neural networks (DCNNs) for detecting TB on chest radiographs using public ChestXray14 as training dataset and Montgomery and Shenzhen as two external testing datasets.
References
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