Data Augmentation for Building an Ensemble of Convolutional Neural Networks
Bioimage classification is important in subcellular localization, accurate cell phenotype recognition, and histopathological classification, to name a few applications. In this paper, we propose an ensemble of deep learning methods built using different batch sizes, different learning rates, and different methods of data augmentation. Our main goal is to test different methods of data augmentation for building an ensemble that boosts the performance of Convolutional Neural Networks (CNN). Our method is evaluated on a diverse set of bioimage classification problems, with each represented by a benchmark dataset and with each bioimage classification task representing a typical cellular or tissue-level classification problem. The results on these datasets demonstrate that the proposed ensemble does indeed boost the performance of the standard CNN.
Information Technology and Cybersecurity
Convolutional neural networks, Deep learning, Microscopy imaging classification, Support vector machines
Nanni, Loris, Sheryl Brahnam, and Gianluca Maguolo. "Data Augmentation for Building an Ensemble of Convolutional Neural Networks." In Innovation in Medicine and Healthcare Systems, and Multimedia, pp. 61-69. Springer, Singapore, 2019.
Smart Innovation, Systems and Technologies
The MATLAB code of all the descriptors and experiments reported in this paper is available at https://github.com/LorisNanni.