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Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Authors :
Jaqueline J. Tenguam
Leonardo H. da Costa Longo
Guilherme F. Roberto
Thaína A. A. Tosta
Paulo R. de Faria
Adriano M. Loyola
Sérgio V. Cardoso
Adriano B. Silva
Marcelo Z. do Nascimento
Leandro A. Neves
Source :
Applied Sciences, Vol 14, Iss 3, p 1084 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.8a07eeff21964a2294c7cedaaa84c888
Document Type :
article
Full Text :
https://doi.org/10.3390/app14031084