Revolutionizing Deep Learning: A Comprehensive Exploration and Comparison of Cutting-edge Data Augmentation Techniques

As the demand for resilient deep learning models in medical imaging

grows, the importance of data augmentation becomes increasingly evident. This study provides a

comprehensive comparative analysis of advanced data augmentation techniques applied to a skin

melanoma dataset.

Through rigorous experimentation and evaluation, we aim to demonstrate the effectiveness

of these methods in enhancing model generalization and reducing overfitting. The performance

metrics selected, including accuracy, precision, recall, and F1-score, were chosen based on

their relevance to assessing model robustness and generalization capability.

Our findings offer valuable insights into the strengths and limitations of various advanced

data augmentation techniques, aiding researchers and practitioners in making informed

choices tailored to their specific applications.

Additionally, this study reviews several patents related to data augmentation in deep

learning, ensuring that our approach is grounded in current technological advancements.