论文标题
部分可观测时空混沌系统的无模型预测
Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform
论文作者
论文摘要
乳腺癌是女性最常见的恶性肿瘤。它占新的恶性肿瘤病例的30％。尽管乳腺癌的发生率仍然很高，但死亡率已不断降低。这主要是由于分子生物学技术的最新发展以及综合诊断和标准治疗水平的提高。乳房X线摄影的早期检测是其中不可或缺的一部分。可能表明乳腺癌的最常见乳房异常是肿块和钙化。以前的检测方法通常获得相对较高的灵敏度，但特异性不令人满意。我们将研究一种应用离散小波变换的方法，傅立叶变换来解析图像并提取表征图像内容的统计特征，例如平均强度和强度的偏度。天真的贝叶斯分类器使用这些功能来对图像进行分类。我们希望获得最佳的高特异性。
Breast cancer is in the most common malignant tumor in women. It accounted for 30% of new malignant tumor cases. Although the incidence of breast cancer remains high around the world, the mortality rate has been continuously reduced. This is mainly due to recent developments in molecular biology technology and improved level of comprehensive diagnosis and standard treatment. Early detection by mammography is an integral part of that. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. Previous detection approaches usually obtain relatively high sensitivity but unsatisfactory specificity. We will investigate an approach that applies the discrete wavelet transform and Fourier transform to parse the images and extracts statistical features that characterize an image's content, such as the mean intensity and the skewness of the intensity. A naive Bayesian classifier uses these features to classify the images. We expect to achieve an optimal high specificity.
