In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and -score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

中文翻译：

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使用人工智能算法开发用于诊断黑色素瘤皮肤病变的识别系统

近年来，计算机化的生物医学成像和分析变得非常有前景、更有趣且非常有益。它们为皮肤病变的诊断提供了显着的信息。现代诊断系统的发展可以帮助在早期阶段检测黑色素瘤，从而挽救许多人的生命。使用先进人工智能的计算机辅助诊断 (CAD) 系统的设计也有显着增长。本研究的目的是开发一种诊断皮肤癌的系统，该系统将导致皮肤癌的高水平检测。所提出的系统是使用深度学习和传统的人工智能机器学习算法开发的。皮肤镜图像是从 PH2 和 ISIC 2018 中收集的，以检查诊断系统。开发的系统分为基于特征和深度学习。基于特征的系统是基于特征提取方法开发的。为了从皮肤镜图像中分割病灶，提出了主动轮廓法。使用混合特征提取处理这些皮肤病变，即局部二进制模式（LBP）和灰度共生矩阵（GLCM）方法来提取纹理特征。然后使用人工神经网络 (ANN) 算法处理获得的特征。在第二个系统中，卷积神经网络（CNNs）算法被应用于皮肤疾病的高效分类；CNN 使用大型 AlexNet 和 ResNet50 迁移学习模型进行了预训练。实验结果表明，所提出的方法优于 HP2 和 ISIC 2018 数据集的最新方法。标准评估指标，如准确性、特异性、敏感性、精确度、召回率和-分数被用来评估两个提议的系统的结果。与 CNN 模型相比，ANN 模型在 PH2 (97.50%) 和 ISIC 2018 (98.35%) 上实现了最高准确率。介绍了黑色素瘤分类和检测的评估和比较、建议的系统。