基于神经网络分类的皮肤癌自动早期检测研究

Ho Tak Lau, Adel Al-Jumaily
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引用次数: 97

摘要

本文开发了一种皮肤癌自动分类系统,研究了不同类型的神经网络对皮肤癌图像进行不同类型预处理的关系。采集到的图像被输入到系统中,并跨越不同的图像处理程序来增强图像的性能。然后将正常皮肤从受影响的皮肤区域移除,癌细胞留在图像中。从这些图像中提取有用的信息,并传递给分类系统进行训练和测试。在包括皮肤镜照片和数码照片的图像数据库中,三层反向传播神经网络分类器的识别准确率为89.9%,自关联神经网络的识别准确率为80.8%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo
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