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The new type of Coronavirus disease called COVID-19 continues to spread quiterapidly. Although it shows some specific symptoms, this disease, which can showdifferent symptoms in almost every individual, has caused hundreds of thousandsof patients to die. Although healthcare professionals work hard to preventfurther 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 ofconvolutional neural network (CNN) architecture, which is the most effectiveimage analysis method of today, is proposed to fulfill the mentioned COVID-19detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16architectures. Then, features in the last layer of these two architectures arecombined with feature fusion. These new image features matrices obtained withfeature fusion are classified for COVID detection. A multi-layer perceptron(MLP) structure optimized by the whale optimization algorithm is used for theclassification process. The obtained results show that the performance of theproposed framework is almost 4.5% higher than VGG-16 performance and almost3.5% higher than ResNet-50 performance.

Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP