Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL‐DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. In general, the results indicate that the proposed method achieves high accuracy and practicality in skin lesion classification. Our model shows excellent performance in comparison with other existing models, which makes it significant for research and application in dermatology diagnosis.

中文翻译：

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基于灰狼优化算法的自适应权重搜索皮损集成分类方法

皮肤癌是一种常见的恶性肿瘤，尤其是黑色素瘤，严重威胁患者的生命和健康。它可能会扩散到身体的其他部位，导致严重的并发症和死亡。在医学领域，皮肤病变图像的准确识别对于诊断不同的疾病至关重要。然而，由于不同皮损之间的相似性，给医学诊断带来了一些挑战。本文提出了一种基于深度学习和优化算法的新型集成学习模型（EL-DLOA），该模型使用四种不同的深度神经网络架构来生成类的置信度，并使用优化算法将这些置信度集成到做出最终的预测。为了保证模型的准确性和可靠性，首先使用三种不同的学习率进行训练，以找到模型的最佳分类性能。然后，提出一种基于灰狼优化算法的新搜索方法，以提高灰狼的搜索效率。该方法通过随机扰动或自适应变异改变灰狼个体位置来改进搜索机制，解决了灰狼算法容易陷入局部最优的问题。最后，使用四种不同的集成策略来减少分类过程中的个体模型偏差。所提出的模型使用公开数据集 HAM10000 进行训练和评估。实验结果表明，改进的灰狼优化算法有效避免了早熟收敛问题，提高了搜索组合效率。此外，在集成方法中，自适应权重平均集成策略有效提高了分类性能，准确率、精度、召回率和 F1 分数分别为 0.888、0.837、0.897 和 0.862。这些指标显示出相对于性能最佳的单一模型不同程度的改进。总的来说，结果表明该方法在皮肤病变分类方面具有较高的准确性和实用性。与其他现有模型相比，我们的模型表现出优异的性能，这对于皮肤病诊断的研究和应用具有重要意义。