Deep learning for actinic keratosis classification

Loris Nanni; Michelangelo Paci; Gianluca Maguolo; Stefano Ghidoni; Loris Nanni; Michelangelo Paci; Gianluca Maguolo; Stefano Ghidoni

doi:10.3934/ElectrEng.2020.1.47

AIMS Electronics and Electrical Engineering

2020, Volume 4, Issue 1: 47-56. doi: 10.3934/ElectrEng.2020.1.47

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Research article

Deep learning for actinic keratosis classification

1.
Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy
2.
Faculty of Medicine and Health Technology, Tampere University

Received: 15 September 2019 Accepted: 18 November 2019 Published: 27 November 2019

Classification of biological images plays a crucial role in many biological problems, e.g. recognition of cell phenotypes and maturation levels, localization of cell organelles and histopathological classification, and holds the potential to support early diagnosis, which is critical in disease prevention. In this paper, we tested different ensemble of canonical and deep classifiers to provide accurate identification of actinic keratosis (AK), one of the most common skin lesions that could degenerate into lethal squamous cell carcinomas.
We used a clinical image dataset to build and test different ensembles of support vector machines trained by handcrafted descriptors and convolutional neural networks (CNNs) for which we experimented different learning rates, augmentation techniques (e.g. warping) and topologies.
Our results show that the proposed ensemble obtains performance comparable to the state of the art. To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni.
- microscopy imaging classification,
- deep learning,
- convolutional neural networks,
- bioimage classifications,
- actinic keratosis
Citation: Loris Nanni, Michelangelo Paci, Gianluca Maguolo, Stefano Ghidoni. Deep learning for actinic keratosis classification[J]. AIMS Electronics and Electrical Engineering, 2020, 4(1): 47-56. doi: 10.3934/ElectrEng.2020.1.47

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Abstract

Classification of biological images plays a crucial role in many biological problems, e.g. recognition of cell phenotypes and maturation levels, localization of cell organelles and histopathological classification, and holds the potential to support early diagnosis, which is critical in disease prevention. In this paper, we tested different ensemble of canonical and deep classifiers to provide accurate identification of actinic keratosis (AK), one of the most common skin lesions that could degenerate into lethal squamous cell carcinomas.
We used a clinical image dataset to build and test different ensembles of support vector machines trained by handcrafted descriptors and convolutional neural networks (CNNs) for which we experimented different learning rates, augmentation techniques (e.g. warping) and topologies.
Our results show that the proposed ensemble obtains performance comparable to the state of the art. To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni.

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