Smart Health Care System for Early Detection of COVID-19 Using X-ray Scans
Source of Publication
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)
The novel Coronavirus spread in the world in December 2019. Millions of people are infected due to this disease. Due to viral illness, daily life routines and the economy are affected in many countries. According to a clinical study, the disease directly attacks the lungs and disturbs the respiratory system. X-ray and CT scans are the main imaging techniques to discover that disease. However, X-ray scans cost is low as comparatively CT scans. In the limited resources, deep learning plays a key role in diagnosing the COVID-19 with the help of X-ray scans. This study proposed a new transfer learning approach based on the convolutional neural network (CNN). We used the four different classes during the experimental process: COVID-19, pneumonia, lung opacity, and viral pneumonia. We also compared our proposed model with other transfer learning-based techniques. Our proposed COVID-TL model attained the best results in terms of classification. The proposed model is a beneficial tool for radiologists to get the early diagnosis results and help the patients in their early stages.
COVID-19, Pulmonary diseases, Computed tomography, Transfer learning, Smart healthcare, Lung, Training data
Mehmood, Atif; Abugabah, Ahed; and Smadi, Ahmad Al, "Smart Health Care System for Early Detection of COVID-19 Using X-ray Scans" (2022). All Works. 5360.
Indexed in Scopus