Machine learning and deep learning for breast cancer risk prediction and diagnosis: a Survey

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Abstract

Breast cancer is the widest spreading disease among women globally. The prevalence rate of breast cancer continued to rise in the last few decades. The mitotic count is a relevant factor for grading invasive breast cancer. Early analysis is an extremely imperative step in treatment. However, it is not an easy one due to several skepticisms in detection which employ mammograms. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. Detailed analysis of breast cancer normally requires medical images of different methods. The sensitivity and specificity of the diagnosis largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. In this paper, the top methodologies used for mitosis detection are analyzed. There are many algorithms for classification and prediction of breast cancer: Support Vector Machine (SVM), Decision Tree (CART), k Nearest Neighbors (KNN), Random Forest (RF), and Bayesian Networks (BN). The Wisconsin data set was used to analyze breast cancer as a training set to assess and measure the performance of the three ML classifiers in terms of key frameworks such as accuracy, recall, precision, and ROC. The outcome obtained in this paper provides a critique of the stateofart ML techniques for breast cancer detection. It was also found that the ensemble classifier gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers. The paper shows how we can use deep learning technology diagnosis of breast cancer using MIAS Dataset. A deep learning approach is almost used for immense task objective Image processing, Computer Vision, Medical Diagnosis, and Neural Language Processing.
机器学习和深度学习用于乳腺癌风险预测和诊断:一项调查
乳腺癌是全球妇女中传播最广泛的疾病。在过去的几十年里,乳腺癌的患病率持续上升。有丝分裂计数是浸润性乳腺癌分级的一个相关因素。早期分析是治疗中极其必要的一步。然而,由于对乳房x光检查的一些怀疑,这并不是一件容易的事。由于它容易受到人为错误的影响,需要更多的时间来完成,并且在有丝分裂的所有阶段细胞核看起来都很相似,因此有丝分裂的自动检测是克服这些问题的一个很好的解决方案。乳腺癌的详细分析通常需要不同方法的医学图像。诊断的敏感性和特异性在很大程度上取决于放射科医生的经验,由于分辨率的限制和对误诊或未发现病变引起的诉讼的担忧,不确定诊断是相当常见的。本文分析了有丝分裂检测的常用方法。乳腺癌的分类和预测算法有很多:支持向量机(SVM)、决策树(CART)、k近邻(KNN)、随机森林(RF)和贝叶斯网络(BN)。威斯康星数据集被用来分析乳腺癌,作为一个训练集,以评估和衡量三个ML分类器在准确率、召回率、精确度和ROC等关键框架方面的表现。本文所获得的结果对目前用于乳腺癌检测的机器学习技术进行了批判。结果表明,集成分类器具有更好的性能。对级联射频和人工神经网络(ANN)进行了初步实验,结果表明分类器的准确率优于单个分类器。本文展示了如何使用MIAS数据集使用深度学习技术诊断乳腺癌。深度学习方法几乎被用于大量的任务目标图像处理、计算机视觉、医学诊断和神经语言处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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