Publication Type : Conference Paper
Publisher : IEEE
Source : IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET2022), pp. 1-5, 13 to 15 September2022, Kota Kinabalu, Malaysia. ISBN: 978-1-6654-6837-4
Url : https://ieeexplore.ieee.org/document/9936750
Campus : Chennai
School : School of Engineering
Department : Electronics and Communication
Year : 2022
Abstract : This work presents the diagnosis of various acute respiratory syndromes using customized CNN architecture from X-ray images. Complications of viral pneumonia results in influenza and COVID-19. The respiratory syndromes occur due to bacterial and fungal infections as well. Hence, the objective was to use customized CNN architecture to perform a multi-class pneumonia classification. VGG16 architecture is carefully trained for pneumonia classification with ReLU activation and categorical cross-entropy loss function. The proposed model is efficient and robust and yielded 97.87% accuracy on the train set and 90% accuracy on the test set. The experimental results suggest that the model efficiently detects all sorts of lung diseases, including COVID 19.
Cite this Research Publication : S Palaniappan, S. Varshaa Sai Sripriya, Amalladinna Rama Lalitha Pranathi, M. Muthulakshmi “Diagnosis of acute respiratory syndromes from x-rays using customised CNN architecture”, IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET2022), pp. 1-5, 13 to 15 September2022, Kota Kinabalu, Malaysia. ISBN: 978-1-6654-6837-4
doi: 10.1109/IICAIET55139.2022.9936750