R Kaur, H GholamHosseini, R Sinha
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1524-1527. doi: 10.1109/EMBC44109.2020.9175391.
Developing a fast and accurate classifier is an important part of a computer-aided diagnosis system for skin cancer. Melanoma is the most dangerous form of skin cancer which has a high mortality rate. Early detection and prognosis of melanoma can improve survival rates. In this paper, we propose a deep convolutional neural network for automated melanoma detection that is scalable to accommodate a variety of hardware and software constraints. Dermoscopic skin images collected from open sources were used for training the network. The trained network was then tested on a dataset of 2150 malignant or benign images. Overall, the classifier achieved high average values for accuracy, sensitivity, and specificity of 82.95%, 82.99%, and 83.89% respectively. It outperfomed other exisitng networks using the same dataset.
开发一个快速准确的分类器是皮肤癌计算机辅助诊断系统的重要组成部分。黑色素瘤是最危险的皮肤癌形式，死亡率很高。黑色素瘤的早期检测和预后可以提高生存率。在本文中，我们提出了一种用于自动黑色素瘤检测的深度卷积神经网络，该网络具有可扩展性，能够适应各种硬件和软件限制。从开源收集的皮肤镜图像用于训练网络。然后在一个包含2150张恶性或良性图像的数据集上对训练好的网络进行测试。总体而言，该分类器的准确率、灵敏度和特异性的平均得分较高，分别为82.95%、82.99%和83.89%。在使用相同数据集的情况下，它的表现优于其他现有网络。
