Accurate multiclass classification of dermoscopic images is critical for early detection of malignant skin lesions, yet existing high-performing deep learning approaches often require computationally heavy backbones, limiting deployment on mobile or point-of-care devices. This study proposes LightSkinNet, a lightweight convolutional neural network (CNN) integrating Dilated Convolutional Enhancement (DCE) blocks, the Convolutional Block Attention Module (CBAM), and Efficient Channel Attention (ECA) to capture multi-scale features and refine salient lesion patterns with minimal computational cost. A hybrid class-balancing pipeline combining mild undersampling and oversampling was applied to the training set, followed by stratified splitting to preserve natural validation/test distributions, and targeted on-the-fly augmentation to enhance generalization. LightSkinNet was benchmarked on the HAM10000 dataset using five-fold stratified cross-validation against baseline models. The proposed model achieved a Top-1 accuracy of \(90.27\%\pm 0.48\) , Top-3 accuracy of \(97.86\%\pm 0.22\) , macro F1-score of \(89.12\%\pm 0.53\) , and Cohen’s Kappa of \(0.872\pm 0.006\) , outperforming all baselines with statistically significant improvements ( \(p<0.05\) ) while maintaining a low parameter count. Gradient-Weighted Class Activation Mapping (Grad-CAM) showed that LightSkinNet focuses on key dermoscopic features, enhancing transparency in AI-assisted diagnosis. These results demonstrate that LightSkinNet delivers an effective accuracy–efficiency trade-off suitable for real-time clinical decision support on resource-constrained platforms.