摘要翻译：
世界卫生组织表示，乳腺癌是全球成年女性癌症死亡的主要原因。尽管乳腺癌在社会和经济发展程度不同的国家不分青红皂白地发生，但在发展中国家和欠发达国家中，由于早期检测技术的缺乏，死亡率仍然很高。从临床的角度来看，乳房X线摄影仍然是最有效的诊断技术，因为这些图像的使用和解释非常广泛。在此，我们提出了一种利用图像感兴趣区域来检测和分类乳腺摄影病灶的方法。我们的建议包括使用多分辨率小波分解每个图像。从每个小波分量中提取Zernike矩。该方法将纹理特征和形状特征相结合，可用于乳腺病变的检测和分类。我们使用IRMA数据库的355张脂肪乳腺组织图像，其中正常233例（无病变），良性72例，恶性83例。采用改进核函数的支持向量机和ELM网络进行分类，以优化分类准确率，达到94.11%。从正确率和训练时间两方面考虑，我们以相反的顺序定义了平均百分比正确率和平均训练时间之间的比值。我们的建议比使用最先进的最佳方法得到的比率高50倍。由于我们提出的模型可以将高准确率和低学习时间相结合，无论何时接收到一个新的数据，我们的工作将能够节省大量的时间和小时，在学习过程中相对于目前最先进的方法。
---
英文标题：
《Detection and classification of masses in mammographic images in a
  multi-kernel approach》
---
作者：
Sidney Marlon Lopes de Lima, Abel Guilhermino da Silva Filho,
  Wellington Pinheiro dos Santos
---
最新提交年份：
2017
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--

---
英文摘要：
  According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high, due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. Herein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases. Classification was performed by using SVM and ELM networks with modified kernels, in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using the best method of the state-of-the-art. As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.
---
PDF链接：
https://arxiv.org/pdf/1712.07116