Authors:
Rafael H. O. Carvalho
1
;
Adriano Silva
1
;
Alessandro Martins
2
;
Sérgio Cardoso
3
;
Guilherme Freire
4
;
Paulo R. de Faria
5
;
Adriano Loyola
3
;
Thaína Tosta
6
;
Leandro Neves
7
and
Marcelo Z. do Nascimento
1
Affiliations:
1
Faculty of Computer Science, Federal University of Uberlândia, Brazil
;
2
Federal Institute of Triângulo Mineiro, Brazil
;
3
Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia, Brazil
;
4
Department of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal
;
5
Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia, Brazil
;
6
Science and Technology Institute, Federal University of São Paulo, Brazil
;
7
Department of Computer Science and Statistics (DCCE), São Paulo State University, Brazil
Keyword(s):
Dysplasia, Fractal Geometry, Reshape, Convolutional Neural Network, Ensemble, Histological Image.
Abstract:
Oral cavity lesions can be graded by specialists, a task that is both difficult and subjective. The challenges in defining patterns can lead to inconsistencies in the diagnosis, often due to the color variations on the histological images. The development of computational systems has emerged as an effective approach for aiding specialists in the diagnosis process, with color normalization techniques proving to enhance diagnostic accuracy. There remains an open challenge in understanding the impact of color normalization on the classification of histological tissues representing dysplasia groups. This study presents an approach to classify dysplasia lesions based on ensemble models, fractal representations, and convolutional neural networks (CNN). Additionally, this work evaluates the influence of color normalization in the preprocessing stage. The results obtained with the proposed methodology were analyzed with and without the preprocessing stage. This approach was applied in a data
set composed of 296 histological images categorized into healthy, mild, moderate, and severe oral epithelial dysplasia tissues. The proposed approaches based on the ensemble were evaluated with the cross-validation technique resulting in accuracy rates ranging from 96.1% to 98.5% with the non-normalized dataset. This approach can be employed as a supplementary tool for clinical applications, aiding specialists in decision-making regarding lesion classification.
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