| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03024 | |
| Number of page(s) | 7 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203024 | |
| Published online | 29 July 2020 | |
Detection of Thoracic Diseases using Deep Learning
1 B.E student, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, Vashi, Navi Mumbai, India
2 Associate Professor, Department of Computer Engineering, Agnel Charities’ Fr. C. Rodrigues Institute of Technology, Vashi, Navi Mumbai, India
The study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them. However, today with the increase in the number of thoracic patients, a quick method to classify the disease and generate the report has become necessary. Also, patient history has to be considered for diagnosis. This paper offers a comparative study on the various deep learning techniques that can process chest x-rays and are capable of detecting the different thoracic diseases. Also, a technique has been proposed to classify 14 diseases namely Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Pleural thickening based on the given X-rays using Residual Neural Network.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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