COVID-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks

Karacan H., Eryılmaz F.

Journal of advanced research in natural and applied sciences (Online), cilt.7, sa.4, ss.486-503, 2021 (Hakemli Dergi)

Özet

The coronavirus pandemic, which emerged at the end of 2019, continues to be effective. Although various vaccines have been developed, the diagnosis of the disease and the 14-day isolation method still remain valid. Purpose: To classify diseases such as COVID-19, tuberculosis, other pneumonias, and lung opacity. For this goal, it was aimed to automate disease diagnosis, which was done manually by expert radiologists with chest X-Ray images, with a hybrid model created with the four most commonly used convolutional neural networks. In addition, it is aimed to reach a more accurate result by combining four CNN models instead of one model during the epidemic when diagnosis is of critical importance. Method: In the binary classification, the images were categorised as COVID-19- positive and negative, and multi-class classification was conducted according to labels including Lung Opacity, COVID-19, Normal, and Viral Pneumonia. The recommended model consisted of MobileNetV2, DenseNet121, InceptionResNetV2, and Xception networks. Transfer learning was used for initial weights. These models, were combined with ensemble learning to obtain better classification performance. Results: The best binary classification result was obtained from the MobileNetv2 with an accuracy rate of 98.84%. The hybrid model improved slightly, with a value 0.16. In the multi-class classification, the DenseNet121 accomplished the highest classification rate, with 93.17%. The hybrid model improvement in the multi-class classification was 0.81. According to these results, the proposed method will alleviate the burden of health personnel during the epidemic. It will also aid in the diagnosis of COVID-19 in areas where facilities are limited.