Malignant melanoma is one of the most deadly forms of skin cancer, which is one of the world's fastest-growing cancers. Early diagnosis and treatment is critical. In this study, a neural network structure is utilized to construct a broad and accurate basis for the diagnosis of skin cancer, thereby reducing screening errors. The technique is able to improve the efficacy for identification of normally indistinguishable lesions (such as pigment spots) versus clinically unknown lesions, and to ultimately improve the diagnostic accuracy. In the field of medical imaging, in general, using neural networks for image segmentation is relatively rare. The existing traditional machine-learning neural network algorithms still cannot completely solve the problem of information loss, nor detect the precise division of the boundary area. We use an improved neural network framework, described herein, to achieve efficacious feature learning, and satisfactory segmentation of melanoma images. The architecture of the network includes multiple convolution layers, dropout layers, softmax layers, multiple filters, and activation functions. The number of data sets can be increased via rotation of the training set. A non-linear activation function (such as ReLU and ELU) is employed to alleviate the problem of gradient disappearance, and RMSprop/Adam are incorporated to optimize the loss algorithm. A batch normalization layer is added between the convolution layer and the activation layer to solve the problem of gradient disappearance and explosion. Experiments, described herein, show that our improved neural network architecture achieves higher accuracy for segmentation of melanoma images as compared with existing processes.

恶性黑色素瘤是最致命的皮肤癌之一，也是世界上增长最快的癌症之一。早期诊断和治疗至关重要。在这项研究中，利用神经网络结构为皮肤癌的诊断构建广泛而准确的基础，从而减少筛查错误。该技术能够提高识别通常难以区分的病变（例如色素斑）与临床未知病变的效率，并最终提高诊断准确性。在医学成像领域，一般来说，利用神经网络进行图像分割的情况比较少见。现有的传统机器学习神经网络算法仍然不能完全解决信息丢失的问题，也不能检测边界区域的精确划分。我们使用本文描述的改进的神经网络框架来实现有效的特征学习和令人满意的黑色素瘤图像分割。网络的架构包括多个卷积层、dropout 层、softmax 层、多个滤波器和激活函数。数据集的数量可以通过训练集的轮换来增加。采用非线性激活函数（如ReLU和ELU）来缓解梯度消失问题，并结合RMSprop/Adam来优化损失算法。在卷积层和激活层之间添加批量归一化层，解决梯度消失和爆炸问题。本文描述的实验表明，与现有过程相比，我们改进的神经网络架构在黑色素瘤图像分割方面实现了更高的准确性。