论文标题
使用小波功能的神经网络方法进行乳房X线摄影诊断
Neural networks approach for mammography diagnosis using wavelets features
论文作者
论文摘要
开发了一种监督的数字乳房X线照片诊断系统。诊断过程是通过将图像的数据转换为使用小波多级分解的特征向量来完成的。该矢量用作针对分离不同乳房X线照片类别的特征。建议的模型由旨在根据肿瘤类型和风险水平对乳房X线照片进行分类的人工神经网络组成。通过使用多级分解而不是一个级别的分解来提取特征向量，从我们先前的研究中提高了结果。放射科医生标记的图像用于评估诊断系统。结果非常有前途，并显示了未来工作的指南。
A supervised diagnosis system for digital mammogram is developed. The diagnosis processes are done by transforming the data of the images into a feature vector using wavelets multilevel decomposition. This vector is used as the feature tailored toward separating different mammogram classes. The suggested model consists of artificial neural networks designed for classifying mammograms according to tumor type and risk level. Results are enhanced from our previous study by extracting feature vectors using multilevel decompositions instead of one level of decomposition. Radiologist-labeled images were used to evaluate the diagnosis system. Results are very promising and show possible guide for future work.
