Institute on High-Performance Computing and Networking (ICAR)-National Research Council of Italy (CNR), 80131 Naples, Italy.
Sensors (Basel). 2022 May 24;22(11):3966. doi: 10.3390/s22113966.
The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF-THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer's disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.
图像分类在医学中非常重要。在这方面，深度学习方法在准确性方面表现出色。这些方法的缺点是它们是黑盒，因此没有向用户解释其选择的原因。在医学领域，这种缺乏透明度和信息的情况是黑盒模型的典型特征，这引起了从业者的关注，结果是对深度学习工具的抵制。为了解决这个问题，这里使用了一种不同的基于可解释性概念的机器学习方法来进行图像分类，该方法得益于使用进化算法。它依赖于连续执行两个步骤。第一步接收一组输入图像，并对其进行图像过滤，从而生成一个数值数据集。第二步是一个分类器，其核心是一个进化算法。后者同时进行分类，并自动提取明确的知识，作为一组 IF-THEN 规则。该方法是针对涉及阿尔茨海默病的 MRI 脑图像数据集进行研究的。具体来说，从数据集中提取了两个类别（非痴呆和中度痴呆）和三个类别（非痴呆、轻度痴呆和中度痴呆）数据集。该方法在准确性方面（在两个类别问题上的最佳运行中达到 100%，在三个类别问题上的最佳运行中达到 91.49%）、F 分数（分别为 1.0000 和 0.9149）和马修斯相关系数（分别为 1.0000 和 0.8763）方面都取得了良好的结果。为了确定这些结果的质量，将它们与一组广泛的知名分类器的结果进行了对比。对比的结果是，在这两个问题中，该方法在准确性和 F 分数方面都取得了最佳结果，而对于马修斯相关系数，它在两个类别问题上的结果最好，在三个类别问题上的结果次之。
