LightDermNet: A Lightweight Attention-Augmented CNN for Reproducible Multi-Class Dermoscopic Skin Lesion Classification
Abstract
Background: Automated dermoscopic skin lesion classification presents a persistent challenge in clinical artificial intelligence (AI): achieving reliable multi-class discrimination under severe class imbalance while remaining computationally viable for resource-constrained deployment. Existing approaches predominantly rely on large pretrained architectures evaluated under single train-test splits with global pre-augmentation, introducing data leakage and limiting reproducibility. Objectives: This work presents LightDermNet, a purpose-built lightweight convolutional neural network that integrates depthwise separable convolutions and convolutional block attention modules across four progressive feature-extraction stages, resulting in 104,840 trainable parameters. Methods: Trained on the HAM10000 dataset (10,015 images, seven lesion classes) under a strictly leakage-free five-fold stratified cross-validation protocol with within-fold augmentation, inverse-frequency class weighting, and focal loss (γ = 2.0), Results: LightDermNet achieves a mean cross-validation accuracy of 0.7820 (±0.0050) and a held-out test accuracy of 0.7558, with a macro receiver operating characteristic – area under the curve of 0.9271. Gradient-weighted class activation mapping visualizations confirm that the model consistently localizes diagnostically relevant lesion regions, supporting its interpretability for clinical decision-support applications. Systematic ablation across four training configurations confirms that the joint application of augmentation, class weighting, and focal loss collectively drives performance gains. Conclusions: Benchmark evaluation against MobileNetV2, MobileNetV3Small, DenseNet121, and ResNet50V2 under identical conditions demonstrates that LightDermNet surpasses all baselines in both mean accuracy and cross-fold stability while utilizing 97–99.6% fewer parameters. These findings establish that sub-200K-parameter models trained with rigorous cross-validation can match or exceed pretrained architectures on the HAM10000 benchmark, providing - a reproducible and computationally accessible foundation - for dermoscopic AI deployment, - in telehealth and point-of-care settings.