Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania.
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
PLoS One. 2022 Jul 19;17(7):e0269950. doi: 10.1371/journal.pone.0269950. eCollection 2022.
Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases.
Multi-Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well-known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs' performance in terms of several well-known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications.
We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results.
The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points.
乳腺癌是女性最常见的肿瘤之一，但也是最常治疗的癌症之一。因此，早期检测至关重要，这可以通过常规乳房 X 光检查来实现。本文旨在描述、分析、比较和评估用于从四个数据库中分类乳腺癌图像的三个图像描述符。
多目标进化算法 (MOEAs) 被证明是用于选择和分类问题的有效方法。本文旨在研究知名分类目标的组合，以比较它们在解决非常特定学习问题中的应用结果。实验结果经过实证分析，并得到统计方法的支持。结果以一系列医学图像数据库为例进行说明，但重点是 MOEAs 在多个知名指标方面的性能。选择这些数据库是为了专门具有可靠的人工注释，以便衡量黄金标准分类与各种 MOEA 分类之间的相关性。
我们已经看到，不同的统计测试如何在我们的集合中将一种算法排在其他算法之上，认为它更好。这些发现并不令人惊讶，表明没有单一的黄金标准来比较不同的技术或进化算法。此外，构建元分类器并使用单一有利的指标对其进行评估是极其不明智和不满意的，因为这会扭曲结果。
解决这些缺陷的最佳方法是选择正确的目标和标准集。例如，使用与准确性相关的目标直接与最大化真阳性数量相关联。另一方面，如果选择准确性作为通用指标，主要的分类目标将转移到增加被正确分类的数据点数量。
