基于深度学习神经网络的唇舌癌分类

Satish Bansal, R. S. Jadon, S. K. Gupta
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引用次数: 0

摘要

口腔癌是发展中国家的主要疾病之一,由酒精、烟草产品和吸烟引起,在人体某些部位产生不受控制和异常的细胞。最近,卷积神经网络(CNN)已经帮助医疗行业使用医学图像来发现不同类型的疾病。研究的目的是建立新的CNN模型,用于口腔癌图像的分析,并确定癌变和非癌变图像。本文采用CNN技术和图像处理技术对癌症或非癌症唇舌图像数据集进行分类。使用深度学习方法来开发和检查所提出的CNN模型的性能。对于包含癌性和非癌性唇舌图像的小型Kaggle数据集进行实验,并应用提出的CNN模型(Oral_Cancer_Detection)。Oral_Cancer_Detection模型的检测结果非常有效、准确,且复杂度低。在我们的实验中,Oral_Cancer_Detection模型的准确率达到94%,并且在更短的时间内获得了更好的准确率结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lips and Tongue Cancer Classification Using Deep Learning Neural Network
One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.
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