Assessing Dynamic DQN Models for Efficient Classification on Skin Disease Data

Abstract

The field of dermatology has increasingly embraced artificial intelligence (AI) and machine learning techniques to improve the accuracy and efficiency of diagnosing and classifying skin diseases. Among these techniques, deep learning models, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in medical image analysis. However, CNNs typically require large volumes of labeled data to achieve optimal results, which presents a significant limitation when working with small or imbalanced skin disease datasets.