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.