Deep learning-based classification of acanthosis nigricans as a clinical sign of high insulin resistance in type 2 diabetes mellitus

Abstract

Acanthosis nigricans (AN) is a cutaneous manifestation strongly linked to insulin resistance and often serves as an early indicator of type 2 diabetes mellitus (T2DM). Early detection of AN is therefore essential for preventive healthcare. Conventional diagnosis typically relies on visual inspection and grading, which may be subjective and inconsistent. To address this limitation, this study proposes a computer-aided diagnostic framework based on deep learning for automatic AN detection from skin fold images. A comprehensive evaluation was conducted using nine widely adopted deep learning architectures, namely VGG16, VGG19, Dense-Net121, DenseNet201, ResNet50, ResNet101, MobileNetV2, Xception, and the vision transformer (ViT). To ensure a fair comparison, all models were trained under identical experimental settings. Model performance was assessed using standard classification metrics. The comparative analysis demonstrates that ResNet101 outperformed the other architectures, achieving an accuracy of 94.12% for AN detection, with a precision and recall of 96.55% and an F1-score of 93.33%. Overall, the results highlight the robustness and effectiveness of convolutional neural network (CNN) based models, particularly ResNet101, as non-invasive and objective tools for automated AN screening. This approach has the potential to support early detection of individuals at risk of T2DM and to enhance preventive healthcare delivery.