earticle

영어

Skin cancer, particularly melanoma, poses significant risks due to its high metastatic potential and challenges in early diagnosis. Accurately detecting skin lesions through automated systems is crucial for improving survival rates. This paper does not merely propose a detection method but analyzes the effectiveness of feature extraction for accurate skin lesion classification. Utilizing a dataset from Kaggle, this paper compares the performance of various deep learning models, including Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and ResNet-18. We evaluate the ability to classify skin lesions by training three models on 10,015 images across seven classes. ResNet-18 achieved the highest accuracy of 81.6%, demonstrating its potential for the development of automated diagnostic systems. In contrast, CNN and DNN attained lower accuracies of 72.9% and 70%, respectively, likely due to limitations in their feature extraction capabilities. These results underscore the superior performance of ResNet-18, particularly in its ability to handle complex patterns and deep feature learning, which are critical for skin lesion classification. In addition, we explored the potential integration of Large Language Model(LLM) to enhance the interpretability of diagnostic outcomes. By utilizing the Llama2 model API provided by Hugging Face, we explained the feasibility of interpreting ResNet-18's predictions to provide users with more transparent and higher-level medical insights. This suggests a promising future direction for improving the explainability and clinical applicability of AI-driven skin lesion diagnosis.