【摘要】 作为皮肤癌黑色素瘤主要检查手段的皮肤镜图像存在显著性低、类内差异大和样本数据量少等问题,难以采用传统算法实现高准确的识别。深度学习算法引入皮肤癌症检测,提出了一种基于深度残差网络的黑色素瘤识别算法。该算法通过构建深度残差网络提取皮肤镜图像的高维特征,使用残差学习防止网络梯度退化、降低网络训练的难度,实现了黑色素瘤的有效识别。相关仿真实验结果表明:所提出的基于深度残差网络的黑色素瘤识别算法性能明显优于基于卷积神经网络传统的算法,具有更高的准确性、敏感性、特异性和鲁棒性。更多还原

【Abstract】 The detection of skin cancer melanoma in dermoscopic images has the following problems: low significance, large intraclass differences, and less sample data. It is difficult to achieve high-accuracy melanoma recognition using traditional algorithms. In this paper, deep learning algorithm is introduced into skin cancer detection, and a melanoma identification algorithm based on deep residual network is proposed. The algorithm extracts high-dimensional features of dermoscopic images by constructing a deep residual network. Using residual learning can prevent network gradient degradation and reduces the difficulty of network training, so it can achieve effective identification of melanoma. The results of relevant simulation experiments show that the performance of melanoma recognition algorithm based on deep residual network presented in this paper is obviously superior to the traditional algorithm based on convolutional neural network. The proposed algorithm has higher accuracy, sensitivity, specificity and robustness in melanoma recognition.更多还原