The chest X-ray images are difficult to classify for the radiologists due to the noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters, and thus require multi-advanced GPUs to deploy. In this paper, we are the first to develop the adaptive morphological neural networks to classify chest X-ray images, such as pneumonia and COVID-19. A novel structure, which can self-learn morphological dilation and erosion, is proposed to determine the most suitable depth of the adaptive layer. Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms.

中文翻译：

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使用新型自适应形态神经网络对胸部 X 射线图像进行分类

由于噪声性质，胸部 X 射线图像难以为放射科医生分类。基于卷积神经网络的现有模型包含大量参数，因此需要部署多个高级 GPU。在本文中，我们率先开发了自适应形态神经网络来对胸部 X 射线图像进行分类，例如肺炎和 COVID-19。提出了一种新的结构，可以自学习形态膨胀和腐蚀，以确定自适应层的最合适深度。胸部 X 光片和 COVID-19 数据集的实验结果表明，与现有模型相比，所提出的模型可以实现最高的分类率。此外，它可以将现有模型的计算参数显着减少 97%。