基于学习向量量化和小波变换的宫颈癌检测

Dhimas Arief Dharmawan, Latifah Listyalina
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引用次数: 0

摘要

宫颈癌已成为世界范围内常见的女性疾病。大多数情况下,宫颈癌是最近才被发现的,因为在早期很难发现。本文设计了一种基于学习向量量化(LVQ)的计算机软件作为宫颈癌早期检测辅助工具。检测前有预处理、对比度拉伸、中值滤波、形态学运算、图像分割和基于小波变换的特征提取六种方法。在这项工作中,使用了73张宫颈细胞图像,其中包括50张正常图像和23张癌图像。使用35张正常图像和14张肿瘤图像训练LVQ。然后在检测过程中使用23张正常图像和9张癌图像。我们的结果表明,88.89%的癌症图像可以被正确检测(灵敏度),100%的正常图像可以被正确检测(特异性),95.83%的整体检测(准确性)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Cervical Cancer Based on Learning Vector Quantization and Wavelet Transform
Cervical cancer has became the common women dsease in the world. Mostly, cervical cancer has been already known lately, because it is very dificult to detect this in early stage. In this work, a computer based software using Learning Vector Quantization (LVQ) has been designed as the early cervical cancer detection aid tool. There are six methods before the detection is performed, namely preprocessing, contrast stretching, median filtering, morphology operation, image segmentation, and Wavelet Transform based feature extraction. In tihis work, 73 cervical cell images that consist of 50 normal images and 23 cancer images are used. 35 normal images and 14 cancer images are used to train the LVQ. Then, 23 normal images and 9 cancer images are used in the testing process. Our results show 88,89 % cancer image can be detected correctly (sensitivity), 100 % normal image can be detected corerctly (specificity), and 95,83 % for overall detection (accuracy).
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