Ensemble of Convolutional Neural Networks for Bioimage Classification. Applied Computing and Informatics


This work presents a system based on an ensemble of Convolution Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of CNN by building an ensemble of CNNs whose scores are combined by sum rule. Several types of ensembles are considered, with different CNN topologies included along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage/medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

Keywords:Convolutional neural networks, bioimage classification, general purpose classifier.

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