Skin cancer (SC) stands out as one of the most life-threatening forms of cancer, with its danger amplified if not diagnosed and treated promptly. Early intervention is critical, as it allows for more effective treatment approaches. In recent years, Deep Learning (DL) has emerged as a powerful tool in the early detection and skin cancer diagnosis (SCD). Although the DL seems promising for the diagnosis of skin cancer, still ample scope exists for improving model efficiency and accuracy. This paper proposes a novel approach to skin cancer detection, utilizing optimization techniques in conjunction with pre-trained networks and wavelet transformations. First, normalized images will undergo pre-trained networks such as Densenet-121, Inception, Xception, and MobileNet to extract hierarchical features from input images. After feature extraction, the feature maps are passed through a Discrete Wavelet Transform (DWT) layer to capture low and high-frequency components. Then the self-attention module is integrated to learn global dependencies between features and focus on the most relevant parts of the feature maps. The number of neurons and optimization of the weight vectors are performed using three new swarm-based optimization techniques, such as Modified Gorilla Troops Optimizer (MGTO), Improved Gray Wolf Optimization (IGWO), and Fox optimization algorithm. Evaluation results demonstrate that optimizing weight vectors using optimization algorithms can enhance diagnostic accuracy and make it a highly effective approach for SCD. The proposed method demonstrates substantial improvements in accuracy, achieving top rates of 98.11% with the MobileNet + Wavelet + FOX and DenseNet + Wavelet + Fox combination on the ISIC-2016 dataset and 97.95% with the Inception + Wavelet + MGTO combination on the ISIC-2017 dataset, which improves accuracy by at least 1% compared to other methods.

翻译：皮肤癌是最具生命威胁的癌症类型之一，若未能及时诊断与治疗，其危险性将显著加剧。早期干预至关重要，因其能为更有效的治疗方案创造条件。近年来，深度学习已成为早期检测与皮肤癌诊断领域的有力工具。尽管深度学习在皮肤癌诊断中展现出良好前景，但模型效率与准确性仍有较大提升空间。本文提出一种结合优化技术、预训练网络与小波变换的皮肤癌检测新方法。首先，归一化图像将通过Densenet-121、Inception、Xception及MobileNet等预训练网络提取输入图像的层次化特征。特征提取后，特征图将经过离散小波变换层以捕获低频与高频分量。随后集成自注意力模块来学习特征间的全局依赖关系，并聚焦于特征图中最相关的部分。神经元数量与权重向量的优化采用三种新型群体智能优化技术实现，包括改进型大猩猩部队优化器、增强灰狼优化算法及狐狸优化算法。评估结果表明，利用优化算法调整权重向量可显著提升诊断准确率，使其成为皮肤癌诊断的高效方法。所提方法在准确率上取得实质性改进：在ISIC-2016数据集上，MobileNet+小波+FOX与DenseNet+小波+Fox组合达到98.11%的最高准确率；在ISIC-2017数据集上，Inception+小波+MGTO组合达到97.95%的准确率，较其他方法至少提升1%的准确率。