We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves $9.5\%$ better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.
翻译：我们建议对3D MRI 阿尔茨海默氏氏病的3D MRI 图像阿尔茨海默氏病分类应用2DCNN结构。 培训3D进化神经网络(CNN)耗时且计算成本高昂。 我们利用近似级集合将3D MRI 图像卷转换成2D 图像,作为2D CNN 的输入。 我们展示了我们提议的CNN模型比基线3D 模型提高了9.5 $$的阿尔茨海默氏病分类精确度。 我们还显示,我们的方法允许进行有效的培训,仅需要20%的培训时间,而3D CNN 模式则需要20%的培训时间。 代码可以在网上查阅: https://github.com/UkyVision/alzheimer-Project。
相关内容
微信扫码咨询专知VIP会员