Improving Automated PSN Assessment in Type 2 Diabetes: A Study on Plantar Lesion Recognition and Probe Avoidance Techniques

Peripheral Sensory Neuropathy (PSN) affects a large proportion of individuals suffering from type 2 diabetes. To avoid ulceration and other damage to the patient’s feet, regular PSN testing, and assessment must be undertaken. Currently, the Semmes-Weinstein Monofilament Examination (SWME) is one of the most widely accepted techniques for PSN assessment. This process is time-consuming, requires special training, and is prone to errors. The number of type 2 diabetes sufferers globally is growing at alarming rates with healthcare workers under enormous pressure to continue to provide one-to-one regular care. In order to reduce the burden on existing services whilst providing the necessary care to patients, automated approaches for PSN detection provide many advantages. Importantly, with respect to an automated SWME method, there will be areas on the plantar surface where the SWM probe should not be applied i.e., areas with lesions or suspect regions. The research presented in this manuscript conducted a comprehensive analysis of different feature sets and classifiers for the task of lesion classification. Three distinct feature sets Local Binary Pattern (LBP), Mel Frequency Cepstral Coefficients (MFCC), and Scale-Invariant Feature Transform (SIFT) were evaluated across various classifiers, including Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and XGBoost. The results revealed nuanced performances across the combinations of feature sets and classifiers. While each feature set demonstrated strengths, the NB classifier applied to the LBP feature set emerged as the most notable performer with an accuracy score of 100%. This combination achieved perfect accuracy, precision, recall, and F1-score metrics, showcasing its robustness in accurately classifying lesion instances. The 5-fold cross-validation results underscored the stability of NB on the LBP feature set, with a negligible standard deviation, affirming its consistent performance across different data subsets. Additionally, the computational time complexity of 0.91 seconds highlighted its efficiency, making NB on the LBP feature set a practical and reliable choice for real-world applications. Statistical analysis using the one-way ANOVA test revealed significant differences in classifier performance across feature sets, with MFCC resulting in significantly lower accuracy compared to LBP and SIFT, which showed similar performance. The Tukey HSD post-hoc test confirmed these findings, highlighting the crucial role of feature set selection in classifier effectiveness.