A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections - Yildirim, Muhammed; Cinar, Ahmet (Int Information & Engineering Technology Assoc, 2020)
COVID-19 appeared in December 19, 2019 in Wuhan, China. This disease has spread to almost all countries in a short time. Countries take a series of stringent measures, including the prohibition of going out to prevent the virus that spreads COVID-19 disease. In this paper, we aimed to diagnose COVID-19 disease from X_RAY images by using deep learning architectures. In addition, 96.30% accuracy rate has been achieved with the hybrid architecture we have improved. While developing the hybrid model, the last 5 layers of Resnet 50 architecture were ejected. 10 layers were added in place of the 5 layers that were removed. The count of layers, which is 177 in the Resnet50 architecture, has been increased to 182 in the hybrid model Thanks to these layer changes made in Resnet50, the accuracy rate has been increased more. Classification was performed with AlexNet, Resnet50, GoogLeNet, VGG16 and developed hybrid architectures using COVID-19 Chest X-Ray dataset and Chest X-Ray images (Pneumonia) datasets. As a result, when other scientific works in the literature are examined, it is finalized that the improved hybrid method offers better results than other deep learning architectures and can be used in computer-aided systems to diagnose COVID-19 disease.
Keywords: Covid-19; deep learning; image processing; Resnet50; hybrid model