arXiv subjects
Publications and source records attributed to Loris Nanni.
Dopamine (DA) is an organic chemical that influences several parts of behaviour and physical functions. Fast-scan cyclic voltammetry (FSCV) is a technique used for in vivo phasic dopamine release measurements. The analysis of such measurements, though, requires notable effort. In this paper, we present the use of convolutional neural networks (CNNs) for the identification of phasic dopamine releases.
Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing and create three different images for every saliency method. Hence, we create 3x3=9 new images for every original image to train different convolutional neural networks. We evaluate the performance of every preprocessing/network couple and we also evaluate the performance of their ensemble. We test our approach on both a small dataset and the large IP102 dataset. Our best ensembles reaches the state of the art accuracy on both the smaller dataset (92.43%) and the IP102 dataset (61.93%), approaching the performance of human experts on the smaller one. Besides, we share our MATLAB code at: https://github.com/LorisNanni/.
In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Networks (CNNs). The specific animal audio classification problems are i) birds and ii) cat sounds, whose datasets are freely available. We train five different CNNs on the original datasets and on their versions augmented by four augmentation protocols, working on the raw audio signals or their representations as spectrograms. We compared our best approaches with the state of the art, showing that we obtain the best recognition rate on the same datasets, without ad hoc parameter optimization. Our study shows that different CNNs can be trained for the purpose of animal audio classification and that their fusion works better than the stand-alone classifiers. To the best of our knowledge this is the largest study on data augmentation for CNNs in animal audio classification audio datasets using the same set of classifiers and parameters. Our MATLAB code is available at https://github.com/LorisNanni.
Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of ocean ecosystems, the former due to their function at many levels of the oceans food chain, the latter because they provide spawning and nursery grounds to many fish populations. Studying and monitoring plankton distribution and coral reefs is vital for environment protection. In the last years there has been a massive proliferation of digital imagery for the monitoring of underwater ecosystems and much research is concentrated on the automated recognition of plankton and corals. In this paper, we present a study about an automated system for monitoring of underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning pretrained CNN for underwater imagery analysis, the opportunity of using different datasets for pretraining models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. The experimental results are very encouraging, our experiments performed on 5 well-knowns datasets (3 plankton and 2 coral datasets) show that the fusion of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report performance of both single CNN and ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model.
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators. The first step, consisting in skin segmentation is performed according to an existing skin detection approach is performed for skin segmentation, then a second step is carried out consisting in the application of a set of morphological operators to refine the resulting mask. Extensive experimental evaluation performed considering two different detection approaches (one based on deep learning and a handcrafted one) carried on 10 different datasets confirms the quality of the proposed method.
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins. From each 3D protein structure, a large set of 2D projections is generated using the protein visualization software Jmol. This set of multi-view 2D representations includes 13 different types of protein visualizations that emphasize specific properties of protein structure (e.g., a backbone visualization that displays the backbone structure of the protein as a trace of the Cα atom). Each type of representation is used to train a different Convolutional Neural Network (CNN), and the fusion of these CNNs is shown to be able to exploit the diversity of different types of representations to improve classification performance. In addition, several multi-view projections are obtained by uniformly rotating the protein structure around its central X, Y, and Z viewing axes to produce 125 images. This approach can be considered a data augmentation method for improving the performance of the classifier and can be used in both the training and the testing phases. Experimental evaluation of the proposed approach on two datasets demonstrates the strength of the proposed method with respect to the other state-of-the-art approaches. The MATLAB code used in this paper is available at https://github.com/LorisNanni.
The aim of this work is to propose an ensemble of descriptors for Melanoma Classification, whose performance has been evaluated on validation and test datasets of the melanoma challenge 2018. The system proposed here achieves a strong discriminative power thanks to the combination of multiple descriptors. 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 architectures along with different learning parameter sets. Moreover CNN are used as feature extractors: an input image is processed by a trained CNN and the response of a particular layer (usually the classification layer, but also internal layers can be employed) is treated as a descriptor for the image and used for training a set of Support Vector Machines (SVM).