利用多层神经网络探索乳腺癌纹理分析

Aalia Nazir, Hafiz Ullah, Ghulam Gilanie, Shabbir Ahmad, Zahida Batool, Asghar Gadhi
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引用次数: 0

摘要

乳腺癌是全球妇女面临的一个重大健康问题;然而,及时发现可以降低女性发病率和死亡率。早期乳房检查已成为所有妇女的当务之急,不过,在发展中国家,如巴基斯坦,必须有足够的检查设施,在那里,乳腺癌是主要的死亡原因。为了应对这种慢性疾病,已经引入了各种图像处理技术来从数字乳房x光照片中自动诊断乳腺癌。目前的研究使用了来自35名参与者的数据。用于筛查的乳房x光片为正常5例,良性15例,恶性15例。乳房图像由放射科医生标记,系统按正常、良性和恶性分类进行训练。此外,采用基于多层神经网络(MNN)的纹理分析方法区分乳腺正常、良、恶性图像。据报道,在对数字乳房x光照片进行分析后,使用了一种自动方法来检测乳房状况。对正常、恶性、良性乳腺图像的和、均值、方差、标准差、峰度、偏度、能量、熵等统计参数进行计算、分析和比较。分析后的结果显示准确度为100%。提取的统计参数的结果在区分正常、恶性和良性乳房x光检查方面是有希望和可靠的,这再次表明需要早期发现疾病,以尽量减少妇女患乳腺癌的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks
Breast cancer is a significant health problem for women globally; however, timely detection can reduce female morbidity and mortality. Early breast screening has become imperative for all women, though, adequate screening facilities are necessarily required in developing countries like Pakistan, where breast cancer is a leading cause of death. To encounter this chronic disease, various image processing techniques have been introduced to automatically diagnose breast cancer from digital mammograms. The current study deployed data from a population of 35 participants. The mammograms used for screening were 5 normal, 15 benign, and 15 malignant patients. The breast images were marked by the radiologist and the system was trained with normal, benign, and malignant classes. Moreover, Multilayer Neural Networks (MNN) based texture analysis methodology was adopted to distinguish normal, benign, and malignant breast images. Reportedly, an automated approach was used to detect breast conditions after conducting the analysis of digital mammograms. Statistical parameters, namely sum, mean, variance, standard deviation, kurtosis, skewness, energy, and entropy were calculated, analyzed, and compared for the normal, malignant, and benign breast images. The results indicated a 100% accuracy after the analysis. The results of the extracted statistical parameters were promising and reliable in distinguishing between normal, malignant, and benign breast mammograms, again indicating the need for early detection of the disease to minimize the risk of breast cancer among women.
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