# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 4 Discussion and Conclusions  
As previously mentioned, there are research works, reported in the literature, that apply machine learning or deep learning techniques with good performance results (taking into account the presented metrics) to analyze the temperature distribution on the soles of the feet of diabetic and healthy individuals, using thermograms. These works either aim to classify the thermograms into only two classes--(1) normal/healthy and (2) diabetics-- or proceed with a more differentiated classification of the diabetic class. The works of [PERSON] et al. (2013); [PERSON] et al. (2016) and [PERSON] (1988) use techniques that only allow for a binary classification of thermograms (fitting the first case), while the works of [PERSON] et al. (2019) and of [PERSON] et al. (2016) (that only use part of the database ([PERSON] et al., 2016), as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Class** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** \\ \hline \multirow{4}{*}{Model 1} & Healthy & 0.833 & 0.667 & 0.896 & 0.706 & 0.686 \\  & 1 & 0.803 & 0.611 & 0.875 & 0.647 & 0.629 \\  & 2 & 0.939 & 0.938 & 0.940 & 0.833 & 0.882 \\  & 3 & 0.970 & 0.929 & 0.981 & 0.929 & 0.929 \\  & Average & 0.886 & 0.786 & 0.923 & 0.779 & 0.781 \\ \hline \multirow{4}{*}{Model 2} & Healthy & 0.924 & 0.833 & 0.958 & 0.882 & 0.857 \\  & 1 & 0.894 & 0.778 & 0.938 & 0.824 & 0.800 \\ \cline{1-1}  & 2 & 0.939 & 0.938 & 0.940 & 0.833 & 0.882 \\ \cline{1-1}  & 3 & 0.970 & 0.929 & 0.981 & 0.929 & 0.929 \\ \cline{1-1}  & Average & 0.932 & 0.869 & 0.954 & 0.867 & 0.867 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between Model 1 and Model 2.  
they only involve the classification of thermograms of diabetic individuals into five classes) are in the second group, although in [9] only two by two classes comparisons are performed. The work developed in this paper aimed to obtain a thermogram classification tool that would allow a response with clinical interest, in the sense of being suitable for being used by health professionals in a clinical environment. Thus, it was intended to develop a tool that is prepared to receive a thermogram at the entrance and that gives as output its classification as healthy, mild diabetic, moderate diabetic or severe diabetic. To this end, the 334 thermograms in the public dataset [15] were used to develop two approaches that allow for the classification of foot thermograms into four classes, either directly, using Model 1, or in two phases, with Model 2. Both models use a one-vs.-all method for class comparison and present good performance (given the obtained values of the metrics); therefore, with this work, we think that we have taken a step towards obtaining a tool that can be used in clinical practice to efficiently classify a diabetic foot. To synthetize what has been conducted, it is observed that, in this article, a classification methodology is proposed, comparing the performance of two different models, and different machine learning algorithms to classify foot thermograms of healthy and diabetic individuals are presented. For the extraction of the features, the concept of cluster was used both to divide the thermograms into different regions and to extract different regional parameters that are used as features to train and test the models used. The two proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes. It can be concluded that Model 2, which firstly classifies the feet as healthy or diabetic and then obtains a refinement of the diabetic ones in three different classes, outperforms Model 1, which classifies in just a step a thermogram into four classes (healthy and three other classes for diabetics), allowing for a more precise classification regarding the healthy category and the first class of diabetics, corresponding to an early stage of the disease.