文章：

基于元启发式算法与深度学习模型的物联网辅助智能皮肤癌检测

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model

原文发布日期：17 October 2023

DOI: 10.3390/cancers15205016

类型: Article

开放获取: 是

英文摘要：

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

摘要翻译： 

物联网辅助的皮肤癌识别技术整合了多种互联设备与传感器，用于支持皮肤状况的初步分析与监测。由于皮损尺寸形态各异、色彩光照差异及皮肤表面反光等因素，皮肤癌图像的初步分析极具挑战性。近年来，基于物联网技术并融合深度学习算法的皮肤癌识别系统已被应用于提升皮肤癌的早期分析与监测水平。本文提出一种物联网环境下基于优化深度学习的皮肤癌检测与分类方法。该方法的核心在于结合元启发式超参数选择策略与深度学习模型实现皮肤癌分类。具体而言，该方法采用算术优化算法与EfficientNet模型进行特征提取，运用堆叠降噪自编码器分类模型进行皮肤癌检测，并最终通过蜻蜓算法实现堆叠降噪自编码器超参数的优化选择。基于国际皮肤影像协作组织皮肤病变数据库的仿真验证表明，相较于现有模型，该优化方法展现出更优性能，其最高灵敏度达97.74%、特异度达99.71%、准确率达99.55%。该模型可为医疗专业人员（特别是皮肤科医生及其他相关从业者）的皮肤癌诊断流程提供有效辅助。

原文链接：

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model