Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr S M R, Jafari M H, Ward K, Najarian K
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1373-1376. doi: 10.1109/EMBC.2016.7590963.
Melanoma, most threatening type of skin cancer, is on the rise. In this paper an implementation of a deep-learning system on a computer server, equipped with graphic processing unit (GPU), is proposed for detection of melanoma lesions. Clinical (non-dermoscopic) images are used in the proposed system, which could assist a dermatologist in early diagnosis of this type of skin cancer. In the proposed system, input clinical images, which could contain illumination and noise effects, are preprocessed in order to reduce such artifacts. Afterward, the enhanced images are fed to a pre-trained convolutional neural network (CNN) which is a member of deep learning models. The CNN classifier, which is trained by large number of training samples, distinguishes between melanoma and benign cases. Experimental results show that the proposed method is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.
黑色素瘤是最具威胁性的皮肤癌类型，其发病率正在上升。本文提出了一种在配备图形处理单元（GPU）的计算机服务器上实现深度学习系统，用于检测黑色素瘤病变。该系统使用临床（非皮肤镜）图像，可协助皮肤科医生对这种皮肤癌进行早期诊断。在所提出的系统中，可能包含光照和噪声影响的输入临床图像会进行预处理，以减少此类伪像。之后，增强后的图像被输入到作为深度学习模型之一的预训练卷积神经网络（CNN）中。通过大量训练样本训练的CNN分类器可区分黑色素瘤和良性病例。实验结果表明，与现有方法相比，该方法在诊断准确性方面更具优势。
