Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Abstract

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms

“…ML and DL models have been explored to determine relevant features for early detection of DFUs [9,[24][25][26][27]. However, except for a few cases, these studies were derived mainly from the only publicly available dataset, the INAOE dataset (Instituto Nacional de Astrofísica, Óptica y Electrónica) [26], which is composed of thermograms containing the plantar aspect of both feet.…”

“…Recently, a similar dataset was released, STANDUP [28], which provides means for extending the current state of the art by simply increasing the number of samples available to train the ML and DL models. Furthermore, the additional dataset enables the determination of the generalizability of the set of state-of-the-art features previously extracted by classical and DL approaches [27].…”

“…In this work, the same methodology previously described was executed in order to extract a state-of-the-art set of features from infrared thermograms [27]. Four input datasets were considered by merging different datasets for feature extraction.…”

“…The SVM classifier was used as a reference to assess and compare the performance of each set of extracted features from the STANDUP and extended databases. In addition, a comparison was performed between the more relevant and robust features extracted in this work and those extracted using solely the INAOE dataset [27] as well as those proposed in previous studies [9].…”

LncRNA SNHG16 Knockdown Promotes Diabetic Foot Ulcer Wound Healing via Sponging MiR-31-5p

“…Diabetic foot ulcer (DFU) is a chronic and complex foot complication caused by sensory and motor neuropathy in diabetic patients (Li et al 2022b). The worldwide incidence of DFU is about 6.3%, the mortality rate is 11%, and the lifetime probability of developing the disease can be as high as 34% (Hernandez-Guedes et al 2023;Kifelew et al 2020). Statistics indicate that DFU is the leading cause of non-traumatic lower limb amputation, in which more than half of DFU patients develop infections, and 20% of infected patients will require amputation, with a mortality rate of about 22% (Jiang et al 2022;Kim et al 2022).…”