Enhancing the Skin Disease Classification Accuracy base on Weighted Cross-Entropy Method with ISIC Dataset

Early detection of skin diseases is essential for improving patient survival rates and preventing serious harm to people's lives and health. In recent years, there have been advancements in the field of skin disease identification using artificial intelligence, particularly through algorithms like deep learning. Scientists have developed a series of skin disease diagnostic systems that can assist clinical doctors in quickly and effectively identifying and diagnosing different types of skin diseases. In this study, we selected and trained a deep learning network (MobileNetV2) and introduced a weighted cross-entropy loss function to address class imbalance in classification tasks, improving sensitivity to minority classes. Testing over 25,000 skin disease images, the results validated the effectiveness and feasibility of the proposed method, achieving an 84.7% classification accuracy on eight categories in the combined dataset, which is highly acceptable. This method is expected to help dermatologists make more accurate clinical diagnoses in the future, potentially reducing the number of skin disease patients.