Hyperspectral Imaging Combined with Machine Learning Classifiers for Diabetic Leg Ulcer Assessment – A Case Study

Abstract

Diabetic ulcers of the foot are a serious complication of diabetes with huge impact on the patient’s life. Assessing which ulcer will heal spontaneously is of paramount importance. Hyperspectral imaging has been used lately to complete this task, since it has the ability to extract data about the wound itself and surrounding tissues. The classification of hyperspectral data remains, however, one of the most popular subjects in the hyperspectral imaging field. In the last decades, a large number of unsupervised and supervised methods have been proposed in response to this hyperspectral data classification problem. The aim of this study was to identify a suitable classification method that could differentiate as accurately as possible between normal and pathological biological tissues in a hyperspectral image with applications to the diabetic foot. The performance of four different machine learning approaches including minimum distance technique (MD), spectral angle mapper (SAM), spectral information divergence (SID) and support vector machine (SVM) were investigated and compared by analyzing their confusion matrices. The classifications outcome analysis revealed that the SVM approach has outperformed the MD, SAM, and SID approaches. The overall accuracy and Kappa coefficient for SVM were 95.54% and 0.9404, whereas for the other three approaches (MD, SAM and SID) these statistical parameters were 69.43%/0.6031, 79.77%/0.7349 and 72.41%/0.6464, respectively. In conclusion, the SVM could effectively classify and improve the characterization of diabetic foot ulcer using hyperspectral image by generating the most reliable ratio among various types of tissue depicted in the final maps with possible prognostic value.