COVID-19 Teşhisi İçin Derin Topluluk Öğrenmeye Dayalı Modellerde Temel Füzyon Tekniklerinin Etkisi

Öz

Küresel salgın hastalık (pandemi) olarak deklare edilen koronavirüs hastalığı (COVID-19), yeni bir viral solunum yolu hastalığıdır. Hastalık insandan insana damlacık veya temas yoluyla bulaşmaktadır. Hastalığın yayılmasını önlemek için hızlı tanı oranları ile hastalığı erken tespit etmek çok önemlidir. Ancak uzun süren patolojik laboratuvar testleri ve test sonuçlarındaki düşük tanı oranı araştırmacıları farklı teknikleri uygulamaya yöneltmiştir. Radyolojik görüntüleme ile birlikte derin öğrenme tekniklerinin uygulanması bu hastalığın doğru tespitinde oldukça önemli bir yere sahiptir. Bu çalışmada, COVID-19 X-ray veri seti kullanılarak temel füzyon fonksiyonlarının topluluk öğrenme algoritmaları üzerindeki sınıflandırma performansına etkisi araştırılmıştır. Farklı derin öğrenme modellerini birleştirmek için iki farklı topluluk modeli oluşturuldu; Topluluk-1 (Ens-1) ve Topluluk-2 (Ens-2). Bu topluluk modellerinde Maks, Mod, Toplam, Ortalama ve Çarpım gibi temel füzyon fonksiyonları test edilmiştir. Elde edilen değerler incelendiğinde Max ve Product temel füzyon fonksiyonlarının sınıflandırma performansı üzerinde olumlu bir etkiye sahip olduğu görülmektedir. Çoklu sınıflandırmada, hem Ens-1 hem de Ens-2 için Max işlevi sırasıyla %85 ve %86 doğruluk oranıyla öne çıkıyor. Product fonksiyonu, ikili sınıflandırmada %99 ile en yüksek performansı elde etmiştir. Sonuçlar, füzyon yöntemlerinin ikili sınıflandırmada daha iyi sınıflandırma performansı elde edebileceğini göstermektedir.

Anahtar Kelimeler

• COVID-19
• Topluluk öğrenimi
• Transfer öğrenimi
• Füzyon
• Maksimum oylama

The Effect of Basic Fusion Techniques in Deep Ensemble Learning-Based Models For Covid-19 Diagnosis

Abstract

The coronavirus disease (COVID-19), declared as a global epidemic disease (pandemic), is a new viral respiratory disease. The disease is transmitted from person to person through droplets or contact. İt is very important to detect the disease early with rapid diagnosis rates to prevent the spread of the disease. However, long-term pathological laboratory tests and low diagnosis rates in test results led researchers to apply different techniques. Radiological imaging has begun to be used to monitor COVID-19 disease as well as being useful in detecting various lung diseases. The application of deep learning techniques together with radiological imaging has a very important place in the correct detection of this disease. İn this study, the effect of basic fusion functions on classification performance on ensemble learning algorithms was investigated using the COVİD-19 X-ray dataset. Two different ensemble models were created to combine different deep learning models; Ensemble-1 (Ens-1) ve Ensemble-2 (Ens-2). The basic fusion rules of Max, Mode, Sum, Average, and Product were tested in these ensemble models. When the obtained values are examined, it is seen that the Max and Product basic fusion functions have a positive effect on the classification performance. İn multi-classification, the Max function for both Ens-1 and Ens-2 becomes prominent with an accuracy rate of 85% and 86%, respectively. The Product function achieved the highest performance with 99% in binary classification. The results show that the fusion methods can achieve better classification performance in binary classification.

Keywords

• Ensemble learning
• Transfer learning
• Fusion
• Max voting
• Covid 19

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