Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University, Benguerir, Morocco.
Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco.
J Med Syst. 2021 Jan 4;45(1):8. doi: 10.1007/s10916-020-01689-1.
Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods.
乳腺癌（BC）是全球女性死亡的主要原因。它通常影响 40 岁以上的女性。医学图像分析是最有前途的研究领域之一，因为它为乳腺癌等疾病的诊断和决策提供了便利。本文对机器学习（ML）和图像处理（IP）技术在 BC 成像中的应用进行了结构化文献综述（SLR）。根据十个标准，对 2000 年至 2019 年 8 月期间发表的 530 篇论文进行了选择和分析：年份和出版渠道、经验类型、研究类型、医学任务、机器学习技术、使用的数据集、验证方法、性能指标和图像处理技术，包括图像预处理、分割、特征提取和特征选择。结果表明，诊断是最常用的医学任务，深度学习技术（DL）主要用于分类。此外，我们发现分类是调查最多的 ML 目标，其次是预测和聚类。大多数选定的研究使用乳房 X 光片作为成像方式，而不是超声或磁共振成像，使用公共或私人数据集，MIAS 是最常研究的公共数据集。至于图像处理技术，大多数选定的研究通过减少噪声和归一化颜色来预处理其输入图像，其中一些研究使用分割方法通过阈值方法提取感兴趣的区域。对于特征提取，我们注意到研究人员使用经典特征提取技术（例如纹理特征、形状特征等）或 DL 技术（例如 VGG16、VGG19、ResNet 等）提取相关特征，最后少数论文使用特征选择技术，特别是过滤方法。
