融合双模型特征，提升皮肤癌分类准确率
An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique
- 用InceptionV3与DenseNet121分别提取特征，加权融合结果
- 达92.27%准确率，敏感度92.33%，优于现有模型
- 适合医学影像分析与AI辅助诊断研究者参考
皮肤癌是一种由DNA损伤引起的严重且可能致命的疾病。早期检测可显著提高生存率，因此精准诊断至关重要。本研究提出一种基于深度学习（DL）的混合框架，实现对良性与恶性皮肤病变的精确分类。方法包括数据集预处理以提升分类精度，并训练两个独立的预训练深度学习模型：InceptionV3与DenseNet121。通过加权求和规则融合两模型输出结果，系统取得优异性能：检测准确率达92.27%，敏感度92.33%，特异性92.22%，精确度90.81%，F1分数91.57%，超越现有模型，验证了该混合方法的鲁棒性与可信度。本研究在皮肤癌诊断领域具有重要进展，为后续研究提供有力基础。该方法有望通过早期发现挽救大量生命，是抗击皮肤癌的革新性技术。
原文摘要 · Abstract (English)
Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with dataset preprocessing to enhance classification accuracy, followed by training two separate pre-trained DL models, InceptionV3 and DenseNet121. By fusing the results of each model using the weighted sum rule, our system achieves exceptional accuracy rates. Specifically, we achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score, outperforming existing models and demonstrating the robustness and trustworthiness of our hybrid approach. Our study represents a significant advance in skin cancer diagnosis and provides a promising foundation for further research in the field. With the potential to save countless lives through earlier detection, our hybrid deep-learning approach is a game-changer in the fight against skin cancer.
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