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

• URL: http://arxiv.org/abs/2103.09904v1
• Date: Mon, 15 Mar 2021 14:30:12 GMT
• Title: Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP
• Authors: Saban Ozturk, Enes Yigit and Umut Ozkaya
• Abstract summary: 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. The help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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