皮肤癌（SC）是最危险的癌症类型之一，如果不及时治疗，可能会威胁患者的生命。通过早期诊断这种疾病，可以更有效地使用治疗方法并预防疾病的进展。机器学习 (ML) 技术可用作 SCD 的有用且高效的工具。到目前为止，已经提出了多种基于ML技术的自动SCD方法；然而，该研究领域仍然需要应用最优且高效的模型来提高SCD的准确性。因此，在本文中，提出了一种结合优化技术和人工神经网络 (ANN) 的 SCD 方法。该方法包括四个步骤：预处理、分割、特征提取和分类。使用 Kohonen 神经网络执行用于识别病变区域的图像分割，其中使用贪婪搜索算法 (GSA) 增强识别的感兴趣区域 (ROI)。所提出的方法使用卷积神经网络（CNN）从 ROI 中提取特征。此外，为了对特征进行分类，使用了人工神经网络，并通过改进的灰狼优化（IGWO）算法来调整神经元的数量和权重向量。该方法利用概率模型来提高GWO算法的收敛速度。根据评估结果，利用IGWO模型优化ANN的结构和权向量，可以有效提高诊断准确率至少5%。实施该方法并与之前的方法进行性能比较的结果也表明，该方法可以在 ISIC-2016 和 ISIC-2017 数据库中诊断 SC，平均准确率分别为 97.09 和 95.17%；与其他方法相比，准确率至少提高了 0.5%。© 2023。作者。

Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient's life. With early diagnosis of this disease, treatment methods can be used more effectively and the progression of the disease can be prevented. Machine Learning (ML) techniques can be utilized as a useful and efficient tool for SCD. So far, various methods for automatic SCD based on ML techniques have been presented; However, this research field still requires the application of optimal and efficient models to increase the accuracy of SCD. Therefore, in this article, a new method for SCD using a combination of optimization techniques and Artificial Neural Networks (ANNs) is presented. The proposed method includes four steps: pre-processing, segmentation, feature extraction, and classification. Image segmentation for identifying the lesion region is performed using a Kohonen neural network, where the identified region of interest (ROI) is enhanced using the Greedy Search Algorithm (GSA). The proposed method, uses a Convolutional Neural Network (CNN) for extracting features from ROIs. Also, to classify features, an ANN is used, and by the Improved Gray Wolf Optimization (IGWO) algorithm, the number of neurons and weight vector are adjusted. In this method, a probabilistic model is used to improve the convergence speed of the GWO algorithm. Based on the evaluation results, using the IGWO model to optimize the structure and weight vector of the ANN can be effective in increasing the diagnosis accuracy by at least 5%. The results of implementing the proposed method and comparing its performance with previous methods also show that this method can diagnose SC in the ISIC-2016 and ISIC-2017 databases with an average accuracy of 97.09 and 95.17%, respectively; which improves accuracy by at least 0.5% compared to other methods.© 2023. The Author(s).