Department of Information Technology, Faculty of Computers and Informatics, Zagazig, University, Zagazig 44519, Egypt.
Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, KafrElSheikh University, KafrElSheikh, 33511, Egypt.
J Digit Imaging. 2020 Oct;33(5):1325-1334. doi: 10.1007/s10278-020-00371-9.
Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively.
黑色素瘤是一种致命的皮肤癌。不同类型的皮肤损伤之间有很高的相似性，这导致了错误的分类。在早期准确地对皮肤损伤进行分类可以挽救生命。在本文中，提出了一种用于皮肤损伤分类过程的高精度方法。该方法利用带有预训练 AlexNet 的迁移学习。使用原始模型的参数作为初始值，其中我们随机初始化最后三个替换层的权重。该方法使用最新的公共数据集 ISIC 2018 进行了测试。根据获得的结果，可以说该方法取得了巨大的成功，能够将皮肤损伤准确地分为七类。这些类别是黑色素瘤、黑色素细胞痣、基底细胞癌、光化性角化病、良性角化病、皮肤纤维瘤和血管病变。准确性、敏感性、特异性和精度的百分比分别为 98.70%、95.60%、99.27%和 95.06%。
