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A novel approach for classification of abnormalities in digitized mammograms

机译:一种数字化乳房X线照片中异常分类的新方法

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摘要

Feature extraction is an important process for the overall system performance in classification. The objective of this article is to reveal the effectiveness of texture feature analysis for detecting the abnormalities in digitized mammograms using Self Adaptive Resource Allocation Network (SRAN) classifier. Thus, we proposed a feature set based on Gabor filters, fractal analysis, multiscale surrounding region dependence method (MSRDM) to identify the most common appearance of breast cancer namely microcalcification, masses and architectural distortion. The results of the experiments indicate that the proposed features with SRAN classifier can improve the classification performance. The SRAN classifier produces the classification accuracy of 98.44% for the proposed features with 192 images from MIAS dataset.
机译:特征提取是分类中整个系统性能的重要过程。本文的目的是揭示使用自适应资源分配网络(SRAN)分类器的纹理特征分析在检测数字化乳房X线照片中异常的有效性。因此,我们提出了一种基于Gabor滤波器,分形分析,多尺度周围区域依赖方法(MSRDM)的功能集,以识别乳腺癌最常见的外观,即微钙化,肿块和建筑变形。实验结果表明,提出的带有SRAN分类器的特征可以提高分类性能。对于来自MIAS数据集的192张图像,SRAN分类器对建议特征的分类精度为98.44%。

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