## 3 Literature Review
### Decision Fusion  
Fusing the data information is one way to achieve the best classification performance in a pattern recognition task. Based on the level of data processing, fusion strategies can be categorized into three main classes: low-level or data fusion, intermediate-level or feature fusion, and high-level or decision fusion.  
In the first main class, or data fusion, data from different sources are combined to employ a new data source. Combining several data sources will add more valuable information. For example, fusing NIR infrared images and visible images will enrich the information in face recognition. In the second main class, or feature fusion, valuable features  
Figure 8: The basic architecture of EfficientNet.  
are selected and combined. In the third class, or decision fusion, several classification results from each classifier are combined to achieve the final result.  
Decision fusion is defined as one of the fusion schemes where the decisions of multiple classifiers are combined into a final decision [33]. For simplification, the decision fusion is illustrated in Figure 9, where \(x\) represents input images classified using different classification techniques. \(y_{1}\) and \(y_{2}\) are the image classification results using different classification methods. The final decision of this result is defined as \(u_{df}\).  
Decision fusion has been used to enhance and generalize the performance of a machine learning model. [PERSON] et al. [34] used a decision fusion technique to optimize the image quality assessment (IQA) methods. Fusing several IQA methods will improve the accuracy of the image quality assessment results. Furthermore, [PERSON] [35] proposed a decision fusion of Markov random field (MRF) and convolutional neural network (CNN) to enhance the resolution and quality of remote sensing images. In their work, MRF and CNN classified positive and negative results by handling them separately, in which MRF handled the negative region while CNN handled the positive one. The negative and the positive region are correct and incorrect classification results of the remote sensing image pixels, respectively. Score-level fusion is proposed to combine the classification results of Gaussian Mixture Model (GMM), support vector machine (SVM), and a combination of GMM-SVM [36].  
A large number of studies reported the decision fusion performance of CNN-based classification methods [37; 38; 39; 40; 41]. [PERSON] et al. [38] proposed the spectral-spatial decision fusion of CNN classification results to obtain the best classification result based on remote sensing image data. On the other hand, [PERSON] et al. [40] introduced decision fusion for age and gender classification based on conventional neural network and CNN. In the paper, it was confirmed that CNN has superior performance compared with that of conventional machine learning. The Behavior Knowledge Space (BKS) achieved a better fusion classification result compared with that of Naive Bayes (NB) fusion, whereas the Sum rule decision fusion achieved a higher result compared with those of the majority of voting and Naive Bayes.  
Based on the finding by [PERSON] et al. [37], medical image fusion can result in notable performance in medical image processing. Several papers reported that fusing the information of medical images can result in better performance compared with that of a single classification system [39]. In recent years, decision fusion has also been applied in deep learning for multi-purpose medical imaging [41]. Averaging, majority voting, and multi-FCN fusion based are the most widely used decision fusion methods in medical image processing. As proposed by [PERSON] et al. [14], the combination of AdaBoost and Random Forest results in 97% accuracy in classifying DFU thermal images.  
Figure 9: A decision fusion scheme.