FUSED DEEP FEATURES BASED CLASSIFICATION FRAMEWORK FOR COVID-19 CLASSIFICATION WITH OPTIMIZED MLP

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.

Keywords

• COVID-19
• Coronavirus
• Classification
• MLP
• Feature Fusion

Optimize Edilmiş ÇKA ile Covıd-19 Sınıflandırması için Kaynaştırılmış Derin Özelliklere Dayalı Sınıflandırma Çerçevesi

Abstract

COVID-19 adı verilen yeni tip Koronavirüs hastalığı oldukça hızlı yayılmaya devam etmektedir. Bazı spesifik semptomlar gösterse de hemen her bireyde farklı semptomlar gösterebilen bu hastalık yüzbinlerce hastanın hayatını kaybetmesine neden olmuştur. Sağlık uzmanları, daha fazla yaşam kaybını önlemek için çok çalışsalar da, hastalık yayılma oranı çok yüksektir. Bu nedenle Bilgisayar Destekli Teşhis (BDT) ve Yapay Zeka (YZ) algoritmalarının desteği hayati önem taşımaktadır. Bu çalışmada, belirtilen COVID-19 algılama ihtiyaçlarını karşılamak için günümüzün en etkili görüntü analiz yöntemi olan Evrişimli Sinir Ağı (ESA) mimarisinin optimizasyonuna dayalı bir yöntem önerilmiştir. İlk olarak, COVID-19 görüntüleri ResNet-50 ve VGG-16 mimarileri kullanılarak eğitilir. Ardından, bu iki mimarinin son katmanındaki özellikler füzyon işlemi uygulanmıştır. Füzyon işlemi ile elde edilen bu yeni görüntü özellikleri matrisleri, COVID-19 tespiti için sınıflandırılır. Sınıflandırma işlemi için Balina Optimizasyon Algoritması (BOA) ile optimize edilmiş Çok Katmanlı Bir Algılayıcı (ÇKA) yapısı kullanılır. Elde edilen sonuçlar, önerilen çerçevenin performansının VGG-16 performansından neredeyse % 4,5 ve ResNet-50 performansından neredeyse % 3,5 daha yüksek olduğunu göstermektedir.

Keywords

• COVID-19
• Koronavirus
• Sınıflama
• ÇKA
• Özellik Füzyonu

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