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Automatic Tuberculosis Screening using Chest Radiographs
Open accessAbstract
Tuberculosis is considered to be dreadful disease also became greater peril in many regions of the world. Demonising tuberculosis still remains a challenging process where Opportunistic infections in immune compromised HIV/AIDS patients. If it is left untreated, rate of patients with tuberculosis are huge. We have standard diagnostics methods which are not considered to be accurate.They are sluggish and un faithful. An effort towards detection of tuberculosis is made in this paper by automated approach using chest radio graphs. In this method primary step is to segment an image suing texture method and lung region is extracted using graph cut extraction method. For the above said method a set of texture and shape features are formed on the lung region to enable the CXR,then binary classifier is used to detect normal or abnormal images. In proposed method we use Artificial Neural Networks (ANN) for screening and to identify the presence of tuberculosis
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Citations
Cited by
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775
Preparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., & McDonald, C. J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv080 · Cited by 1,164
An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare
Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/4180949 · Cited by 616
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.274 · Cited by 373
Pre-trained convolutional neural networks as feature extractors for tuberculosis detection
Lopes, U. K., & Valiati, J. F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.001 · Cited by 294
Metadata
- Authors
- Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C., Leopauline, S., Kalpana, R., Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C., Sharmila, P., & Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C.
- Venue
- International Journal of Engineering and Advanced Technology
- Publisher
- Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
- Published
- 2019-12-30
- Volume
- 9
- Issue
- 2
- Pages
- 2665-2668
- ISSN
- 2249-8958 (ISSN-L)
- OpenAlex
- W4232097126
- Cited by
- 273
- References
- 0
- FWCI
- 25.50
- Type
- journal article
- Language
- en
- Open access
- diamond
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- The paper
- Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C., Leopauline, S., Kalpana, R., Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C., Sharmila, P., & Department of Electronics and Communication Engineering, V. T. M. T. D. R. D. S. E. C. (2019). Automatic Tuberculosis Screening using Chest Radiographs. International Journal of Engineering and Advanced Technology. https://doi.org/10.35940/ijeat.a1507.129219
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