# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 1 Introduction  
Technological advances in infrared cameras improved the speed of response and resolution, which allowed for the application of this technology to different areas, namely, to medical use [1]. With this technique, it is possible to visualize the temperature distribution of the human body and detect thermal variations associated with some diseases, such as breast cancer, vascular disorders, muscle pain, complications related to diabetic foot, among others [1, 2]. The application of infrared thermography has increased significantly over the years, especially with regard to the study of problems related to diabetic foot [3, 4], that is, one of the main complications observed in Diabetes Mellitus (DM) patients, who have a 12% to 25% risk of developing foot ulcers during life [5, 6]. Currently, medical information systems that support detection and decision making are changing, evolving from a traditional manual data analysis to more modern and specialized computer methods, known as Computer-Assisted Diagnosis (CAD), thus providing tools that allow health professionals to achieve more accurate diagnosis [7]. The evaluation of medical images by humans is susceptible to errors due to several factors, namely: negligence, fatigue, and even sensory overload, as consequence of the large amount of information that needs to be analyzed. Other factors to be consideredare that human visual capacities contain limitations, resulting from the visual perception of optical illusions, which affects the accuracy of the diagnosis. It also happens that health institutions may not have enough specialists to perform the diagnostic task [8]. Therefore, it is important to bridge the gap between the user's perception and the accuracy of the diagnosis; as the literature review shows, the development of CAD systems helps to overcome these problems, with image processing algorithms with greater detection performance based on automated segmentation, image enhancement, and restoration and feature extraction and classification approach [3; 4; 9]. There are several studies that focused on the diagnosis of diabetic foot diseases, using thermal images, based on the measurement of skin temperature variation. Many of them seek to classify foot thermograms through asymmetric analysis, which consists of comparing the temperature of the foot with the contralateral one, in order to define a limit that allows for categorizing between healthy and diabetic [10; 11; 12]. This type of approach has shown good results in several studies; however, it has some limitations, for example, when the patient has similar complications in both feet, as it is not possible to detect the risk areas and incorrectly classify the thermogram. Nevertheless, as techniques to detect areas of risk without contralateral analysis were still needed, as well as to have a consistent accurate algorithm that can effectively detect, analyze and classify the minimum thermal variations in the diabetic foot, machine learning techniques have gained an increasing impact in medical areas. The support vector machine algorithm was used by [PERSON] et al. [13] to apply asymmetric analysis to foot thermograms and classify them as normal or prone to ulcer. To combat the limitations previously mentioned, in [14] the authors propose a thermal change index, which represents the degree of variation of the plantar temperature, to group the thermograms of diabetic individuals in five classes; for that, a public plantar thermogram database [15] was used. This public dataset has been used by several researchers with the aim of analyzing the distribution of the temperature of the feet of (non)diabetic individuals. One of them was [PERSON] et al. [9], who proposed a deep learning technique to classify the thermograms of the feet of diabetic individuals into five classes, by comparing two classes at a time. Another group was [PERSON] et al.