📄 中文摘要
该研究提出了一种基于深度学习的皮肤病变分割与分类方法，旨在提高皮肤疾病的诊断准确性。通过使用卷积神经网络（CNN）对皮肤图像进行处理，系统能够有效地识别和分类不同类型的皮肤病变，包括黑色素瘤和良性病变。研究中采用了大量的标注数据集进行训练，并通过多种评估指标验证了模型的性能。结果表明，该方法在分割精度和分类准确性方面均优于传统方法，具有良好的临床应用潜力。未来的研究将集中在进一步优化算法和扩展数据集，以提升模型的泛化能力和实用性。
皮肤病变的分割与分类用于疾病诊断
出处: Segmentation and Classification of Skin Lesions for Disease Diagnosis
发布: 2026年3月24日
📄 English Summary
Segmentation and Classification of Skin Lesions for Disease Diagnosis
A deep learning-based method for segmentation and classification of skin lesions is proposed to enhance the diagnostic accuracy of skin diseases. Utilizing convolutional neural networks (CNNs) for processing skin images, the system effectively identifies and classifies various types of skin lesions, including melanoma and benign lesions. The study employs a large annotated dataset for training and validates the model's performance through multiple evaluation metrics. Results indicate that the proposed method outperforms traditional approaches in both segmentation precision and classification accuracy, showcasing significant clinical application potential. Future research will focus on further optimizing the algorithm and expanding the dataset to improve the model's generalization capabilities and practicality.