Ensemble of convolutional neural networks for bioimage classification. Issue 1 (16th July 2020)

• Record Type: Journal Article
• Authors: Nannia, Loris; Ghidoni, Stefano; Brahnam, Sheryl
• Is Part Of: Applied computing and informatics. Volume 17:Issue 1(2021)
• Page Start: 19
• Page End: 35
• Publication Date: 2020-07-16
• Subjects: Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2018.06.002
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : This work presents a system based on an ensemble of Convolutional 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 trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies 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 and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni .