Comparative Analysis of EfficientNet-B0 and MobileNetV3-large Architectures for Imbalanced Multiclass Skin Lesion Classification

Abstract

— Automated skin lesion classification systems face significant challenges in handling severe class imbalance characteristic of clinical dermatological datasets. This study presents a comprehensive comparative analysis of two state-of-the-art convolutional neural network architectures — EfficientNet-B0 and MobileNetV3-Large — for multiclass skin lesion classification on the HAM10000 benchmark dataset. The investigation systematically addresses an extreme class imbalance ratio of 58.30:1 through the strategic application of RandomOverSampler methodology combined with transfer learning from ImageNet pretrained weights. We implement identical training protocols for fair architectural comparison, including progressive dropout regularization (0.3→0.2→0.1) and conservative data augmentation strategies. Comprehensive ablation studies demonstrate the cumulative effectiveness of each pipeline component, with class balancing contributing 10.39% improvement and transfer learning providing substantial performance gains. Experimental validation reveals that EfficientNet-B0 achieves 98.86% classification accuracy with balanced precision, recall, and F1-Scores of 98.89%, 98.86%, and 98.85%, respectively, while MobileNetV3-Large delivers comparable performance at 98.88% accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis validates that learned features align with clinical diagnostic criteria, demonstrating attention to lesion boundaries, color variations, and texture patterns. Compared to existing state-of-the-art methods on HAM10000, our approach achieves superior performance with fewer parameters (5.3 M), representing a marked advancement for resource-constrained deployment scenarios. These findings establish a validated framework for managing severely imbalanced medical imaging datasets and provide empirical guidance for efficient architecture selection in dermatological computer-aided diagnosis systems.