## 1 Introduction  
Diabetes mellitus (DM) is one of the major diseases leading to death worldwide. Diabetes is a combination of metabolic illnesses marked by hyperglycemia--a condition of high sugar or glucose concentration in blood- resulting from abnormal insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is linked to long-term damage, dysfunction, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and blood vessels. Diabetic foot ulcer is one of the major complications caused by diabetes mellitus (DM) [1]. Foot ulcers occur due to damaged skin tissues under the big tones and on the foot's plantar. Foot ulcers cause the underlying skin layer to be exposed, affecting the feet to the bone as a result of the complication of uncontrolled diabetes. Improper and late handling of patients with a diabetic foot can result in an amputation of the foot. All diabetic patients can suffer from diabetic foot ulcers; however, the ulcers are preventable with good food care prior to chronic complications. Thus, an early detection of diabetic foot ulcer should be provided. To date, various early detection methods have been proposed. Some worth mentioning are the works conducted by [PERSON] et al. [2] and [PERSON] et al. [3]. However, the proposed methods are less effective, with a maximum accuracy of 96%. In addition, various intelligent telemedicine monitoring systems have been employed to detect diabetic foot ulcer complications automatically [4; 5]. Several approaches have been deployed using the medical image modality of visual images and thermal images. Thermal imaging is one of the most useful approaches in this case. It utilizes foot temperature to analyze foot complications. It is also a non-invasive method and does not cause harm to the body tissue [6]. Meanwhile, some studies have reported diabetic foot ulcer (DFU) classification using conventional machine learning [2; 7; 8]. Support vector machine (SVM) is one type of conventional machine learning techniques used for this purpose. However, according to the work by [PERSON] et al. [9], SVM only achieved 87.5% accuracy using a private dataset. The SVM obtained a recall of 50%, which means only a half-positive patient can be classified as positive. Artificial Neural Networks (ANN) and k-nearest neighbors (kNN) were also applied to detect diabetic foot, yet they obtained lower results than those of SVM. Because the conventional method is still unable to detect and classify diabetic foot effectively, experts developed deep learning as a more effective method used for an image classification task [9]. Recently, experts have developed a classifier by implementing deep learning, as it shows capability in learning the image feature automatically. Several deep learning approaches that had been proposed in DFU detection are DFUNet [8], DFUQUTNet [10], ComparisonNet [11], and Segmentation [12; 13]. However, most of the methods were proposed for the visual (visible) images, except the work by [PERSON] et al. [7], which used thermal images. They [7] proposed a new deep learning architecture to classify five classes of diabetic foot ulcers in DM groups only. It focused only on the risk levels of DFU patients, not on early detection. A previous study on early detection for DFU using thermal images was published by [14]. However, it was reported that an accuracy of only 97% was achieved using a combination of adaboost and random forest (RF) methods. In the current study, we attempt to develop a new deep learning framework for the early detection of diabetic foot using decision fusion and thermal images. Generally, it is difficult to create a single classifier that works effectively on all of the testing data [15]. Therefore, decision fusion can be one of the solutions. Decision fusion can be applied to enhance the generalization of a training model and to avoid any biases in the classification results, particularly when it is trained using a limited dataset.