Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans
Source of Publication
2022 26th International Computer Science and Engineering Conference (ICSEC)
Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better.
COVID-19, Databases, Pulmonary diseases, Transfer learning, Lung, Feature extraction, Corona
Smadi, Ahmad Al; Abugabah, Ahed; Mahenge, Shadrack Fred; and Shahid, Farah, "Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans" (2022). All Works. 5691.
Indexed in Scopus