Modern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way. One of the most important applications is evaluation of CT brain scans, where the most precise results come from deep learning approaches. In this article, we propose a novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture. The support neural network helps CNN to find the most adequate filers for pooling and convolution layers. As a result, the main neural classifier learns faster and reaches higher efficiency. Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall. We have described our proposed mechanism and discussed numerical results to draw conclusions and show future works.

现代医疗诊所使用计算机系统支持医学检查，该计算机系统使用计算智能以更有效的方式检测潜在的健康问题。最重要的应用之一是CT脑扫描的评估，其中最精确的结果来自深度学习方法。在本文中，我们为深层神经网络架构提出了一种新颖的相关学习机制（CLM），该机制将卷积神经网络（CNN）与经典架构相结合。支持神经网络可帮助CNN找到最适合池化和卷积层的文件管理器。结果，主要的神经分类器学习得更快并且达到了更高的效率。结果表明，我们的CLM模型能够达到约96％的精度，以及约95％的精度和召回率。