基于卷积神经网络的恶性黑色素瘤自动分类

José Guillermo Guarnizo, Sebastián Riaño Borda, Edgar Camilo Camacho Poveda, Armando Mateus Rojas
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引用次数: 0

摘要

这项研究提出了一种使用卷积神经网络(CNN)识别黑色素瘤(一种癌症)的架构设计,这项工作将有助于生物医学、机器学习等领域的研究人员在未来的项目中推进他们的研究并改进这项建议。CNN主要用于计算机视觉(人工智能的一个分支),用于皮肤痣的模式识别,并通过有限的数据集确定是否存在恶性黑色素瘤。在这种情况下设计和训练的CNN分类器是通过堆叠的几层卷积和池来构建的,以形成6层的神经网络,然后是完全连接的,以完成具有输出分类器的架构。拟议中的训练我们的CNN的数据库是由国际皮肤成像合作组织(ISIC)提供的最大的黑色素瘤和其他皮肤病变的皮肤镜图像的公开收集,该组织由国际皮肤数字成像学会(ISDIS)赞助,这是一项旨在改进黑色素瘤诊断的国际努力。本研究的目的是设计一种具有高精度的卷积神经网络,以帮助医学专业人员诊断黑色素瘤,在这种情况下,可以获得高达88.75%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Malignant Melanoma Classification Using Convolutional Neural Networks
This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.
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