Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP
This work addresses COVID-19 diagnosis for healthcare applications, but it is incremental as it combines existing methods with optimization.
The authors tackled COVID-19 detection from medical images by proposing a framework that fuses deep features from ResNet-50 and VGG-16 and classifies them with an optimized MLP, achieving performance improvements of about 4.5% over VGG-16 and 3.5% over ResNet-50.
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.