用改进VGG16模型实现90.67%准确率的皮肤病分类。
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning
- 基于ImageNet预训练的改进VGG16，融合数据增强技术。
- 在公开皮肤病数据集上达到90.67%分类准确率。
- 适合医疗资源不足地区做快速辅助诊断。
皮肤疾病可由感染、过敏、遗传因素、自身免疫紊乱、激素失衡或环境诱因（如日晒和污染）引起。某些疾病如光化性角化病和银屑病若未及时治疗可能致命。早期识别至关重要，但现有诊断方法往往昂贵且难以普及。本研究提出一种基于深度学习的新型高效皮肤病诊断方法，采用修改版VGG16卷积神经网络（CNN）。该模型包含多个卷积层，使用ImageNet权重，并对顶层进行重构，加入全连接层与最终softmax激活层以实现分类。实验使用名为“Skin Disease Dataset”的公开数据集。尽管VGG16默认不包含数据增强，但训练前通过旋转、平移和缩放等预处理技术实现了数据扩充。所提方法在测试集上取得90.67%的准确率，证明其在皮肤病分类任务中的可靠性，展现出良好的实际应用前景。
原文摘要 · Abstract (English)
Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Some skin diseases, such as Actinic Keratosis and Psoriasis, can be fatal if not treated in time. Early identification is crucial, but the diagnostic methods for these conditions are often expensive and not widely accessible. In this study, we propose a novel and efficient method for diagnosing skin diseases using deep learning techniques. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. The model includes several convolutional layers and utilizes ImageNet weights with modified top layers. The top layer is updated with fully connected layers and a final softmax activation layer to classify skin diseases. The dataset used, titled "Skin Disease Dataset," is publicly available. While the VGG16 architecture does not include data augmentation by default, preprocessing techniques such as rotation, shifting, and zooming were applied to augment the data prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating its reliability in classifying skin diseases. The promising results highlight the potential of this approach for real-world applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。