  The new type of Coronavirus disease called COVID-19 continues to spread quite
rapidly. Although it shows some specific symptoms, this disease, which can show
different symptoms in almost every individual, has caused hundreds of thousands
of patients to die. Although healthcare professionals work hard to prevent
further loss of life, the rate of disease spread is very high. For this reason,
the help of computer aided diagnosis (CAD) and artificial intelligence (AI)
algorithms is vital. In this study, a method based on optimization of
convolutional neural network (CNN) architecture, which is the most effective
image analysis method of today, is proposed to fulfill the mentioned COVID-19
detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16
architectures. Then, features in the last layer of these two architectures are
combined with feature fusion. These new image features matrices obtained with
feature fusion are classified for COVID detection. A multi-layer perceptron
(MLP) structure optimized by the whale optimization algorithm is used for the
classification process. The obtained results show that the performance of the
proposed framework is almost 4.5% higher than VGG-16 performance and almost
3.5% higher than ResNet-50 performance.
