Enhancing Skin Lesion Classification with Ensemble Data Augmentation and Convolutional Neural Networks

Convolutional neural networks (CNNs) have gained considerable recognition due to their achievements in image classification tasks, notably in the domain of medical imaging. However, the scarcity of accurately labeled datasets, especially concerning skin lesion images, poses significant challenges. The manual annotation of medical images, such as those related to skin lesions, is a labor-intensive process requiring expertise. To overcome the limited availability of labeled data, data augmentation emerges as a crucial solution. This technique involves applying various transformations to the original images, enriching the training dataset and improving the model’s generalization. This chapter provides a comprehensive examination of both traditional and ensemble data augmentation methods tailored specifically for skin lesion classification. Traditional methods encompass geometric and color-space transformations, while ensemble methods combine multiple augmentation strategies. Additionally, besides focusing on data augmentation, this study employs four prominent CNN architectures: ResNet50, MobileNetV2, EfficientNet, and DenseNet. Through empirical analysis, this research underscores the significant impact of data augmentation on model accuracy and robustness, especially when dealing with limited annotated data. These findings contribute to the advancement of automated skin lesion identification, offering potential applications in clinical settings by addressing the challenges posed by data scarcity and annotation complexity.