Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer's disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters.

中文翻译：

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使用基于深度学习的进化方法进行脑MRI分析。

卷积神经网络（CNN）模型最近在医学图像分析中展示了令人印象深刻的性能。但是，对于他们为什么表现如此出色或他们学到了什么，目前尚无明确的了解。本文采用三维卷积神经网络（3D-CNN）将脑MRI扫描分为两个预定义的组。另外，提出了一种基于遗传算法的脑部掩盖（GABM）方法作为一种可视化技术，该技术为3D-CNN的功能提供了新的见识。提出的GABM方法包括两个主要步骤。第一步，使用一组脑部MRI扫描来训练3D-CNN。第二步，应用遗传算法（GA）在MRI扫描中发现知识渊博的大脑区域。知识丰富的区域是3D-CNN大部分用于从中提取重要和有区别特征的大脑区域。为了将GA应用于大脑MRI扫描，提出了一种新的染色体编码方法。已使用ADNI（包括140名阿尔茨海默氏病受试者）和ABIDE（包括1000名自闭症受试者）大脑MRI数据集对提出的框架进行了评估。实验结果表明，ADNI数据集的5倍分类精度为0.85，ABIDE数据集的分类精度为0.70。所提出的GABM方法已经提取了ADNI数据集中的6至65个知识脑区域（以及ABIDE数据集中的15至75个知识脑区域）。这些区域被解释为大脑的部分，而3D-CNN大多将其用于提取脑部疾病分类的特征。