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.
Highlights
Despite strong advances in automatic image analysis in recent years, in the field of medicine, expert clinicians remain the ones who typically make the final diagnostic determination of medical images
Here2: sum rule between (FCN þ NoAlex) and (FH þ CLMþGOLD); before fusion the scores of (FCN þ NoAlex) and FH are normalized to mean 0 and standard deviation 1; In Table 7 we compare our ensemble Here1 with the literature, for a fair comparison we have reported methods based on the same testing protocol used to assess the performance of our approaches
In this work an ensemble of Convolutional Neural Networks (CNNs) is proposed for cancer related color datasets
Summary
Despite strong advances in automatic image analysis in recent years, in the field of medicine, expert clinicians remain the ones who typically make the final diagnostic determination of medical images. Automatic and semi-automatic analysis is gaining in importance, due to the massive growth in medical imaging technologies and thanks to some giant strides in the fields of image processing, pattern recognition, and image classification, all of which have made automatic analysis of medical images a viable alternative [1,2,3]. Bioimage processing often relies on approaches based on feature extraction from images that contain important information for a particular diagnostic task. The full terms of this license may be seen at http://creativecommons. org/licences/by/4.0/legalcode
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