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.

本研究解决了皮肤癌早期检测的准确性问题，提出了一种基于深度学习的混合框架，实现良性与恶性皮肤病变的精确分类。通过结合预训练模型InceptionV3和DenseNet121的结果，我们的系统达到了92.27%的检测准确率，显著超越了现有模型，为皮肤癌的诊断提供了新的有力工具，有潜力挽救众多生命。

使用混合特征融合技术的皮肤癌检测集成深度学习模型