Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Conference Proceedings
Publisher : IEEE
Source : 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA)
Url : https://ieeexplore.ieee.org/abstract/document/9936426
Campus : Amritapuri
School : School of Computing
Year : 2022
Abstract : From past two years world is suffering with the COVID-19 pandemic which mainly infecting human lungs. Lung infection can be caused by different viruses and bacteria, diagnosis of Lung infections can be done using Radiography X-ray images. Due to similarity in infections there are high chances that other infections can be falsely considered as COVID-19. Manual chest X-ray diagnosis for COVID-19 requires a radiologist and a time taking process, hence it is not a good choice as the covid-19 can spread in no time from person to person.Hence there is need for automatic process of Covid-19 detection and classification of different chest disease. We worked on developing a deep transfer learning model which will accurately classify various chest infections such as COVID-19, Lung-Opacity, Tuberculosis, Viral Pneumonia, We used transfer learning approach which uses existing deep learning models and adding our layers for classification. This work compares various pre-trained models and also convolutional neural network by considering data set of different infections with total 4526 images belonging to 5 classes for training, 647 images belonging to 5 classes for validation, 746 images for testing.
Cite this Research Publication : Mohammad, M., & Reddy, M. R. (2022, September). Deep Transfer Learning approach for Classification of Chest Infections in Radiographic X-Ray Images. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.