Oral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks

Rafael H. O. Carvalho, Adriano Silva, Alessandro Martins, Sérgio Cardoso, Guilherme Freire, Paulo R. de Faria, Adriano Loyola, Thaína Tosta, Leandro Neves, Marcelo Z. do Nascimento

2024

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 dataset 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.

Paper Citation

in Harvard Style

H. O. Carvalho R., Silva A., Martins A., Cardoso S., Freire G., R. de Faria P., Loyola A., Tosta T., Neves L. and Z. do Nascimento M. (2024). Oral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 524-531. DOI: 10.5220/0012389000003660

in Bibtex Style

@conference{visapp24,
author={Rafael H. O. Carvalho and Adriano Silva and Alessandro Martins and Sérgio Cardoso and Guilherme Freire and Paulo R. de Faria and Adriano Loyola and Thaína Tosta and Leandro Neves and Marcelo Z. do Nascimento},
title={Oral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={524-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012389000003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Oral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks
SN - 978-989-758-679-8
AU - H. O. Carvalho R.
AU - Silva A.
AU - Martins A.
AU - Cardoso S.
AU - Freire G.
AU - R. de Faria P.
AU - Loyola A.
AU - Tosta T.
AU - Neves L.
AU - Z. do Nascimento M.
PY - 2024
SP - 524
EP - 531
DO - 10.5220/0012389000003660
PB - SciTePress