Classification of biological images is an important task with crucial application in many fields, such as cell phenotypes recognition, detection of cell organelles and histopathological classification, and it might help in early medical diagnosis, allowing automatic disease classification without the need of a human expert. In this paper we classify biomedical images using ensembles of neural networks. We create this ensemble using a ResNet50 architecture and modifying its activation layers by substituting ReLUs with other functions. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish , Mish, Mexican Linear Unit, Gaussian Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign (SRS) and others. As a baseline, we used an ensemble of neural networks that only use ReLU activations. We tested our networks on several small and medium sized biomedical image datasets. Our results prove that our best ensemble obtains a better performance than the ones of the naive approaches. In order to encourage the reproducibility of this work, the MATLAB code of all the experiments will be shared at https://github.com/LorisNanni.

中文翻译：

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卷积神经网络用于医疗保健的激活层不同随机选择的比较

生物图像分类是在许多领域中至关重要的任务，在诸如细胞表型识别，细胞器检测和组织病理学分类等许多领域都至关重要，它可能有助于早期医学诊断，从而无需人类专家即可自动进行疾病分类。在本文中，我们使用神经网络集成对生物医学图像进行分类。我们使用ResNet50架构创建此集合，并通过用其他功能替换ReLU来修改其激活层。我们从以下激活中选择我们的激活：ReLU，泄漏ReLU，参数ReLU，ELU，自适应Piecewice线性单位，S形ReLU，Swish，Mish，墨西哥线性单位，高斯线性单位，参数可变形线性单位，软根符号（ SRS）等。作为基准 我们使用了仅使用ReLU激活的神经网络的集合。我们在几个中小型生物医学图像数据集上测试了我们的网络。我们的结果证明，我们最好的合奏要比单纯的方法获得更好的性能。为了鼓励这项工作的可重复性，所有实验的MATLAB代码将在https://github.com/LorisNanni上共享。