Classification of Histopathological Images of Penile Cancer using DenseNet and Transfer Learning

Marcos Gabriel Mendes Lauande, Amanda Mara Teles, Leandro Lima da Silva, Caio Eduardo Falcão Matos, Geraldo Braz Júnior, Anselmo Cardoso de Paiva, João Dallyson Sousa de Almeida, Rui Miguel Gil da Costa Oliveira, Rui Miguel Gil da Costa Oliveira, Haissa Oliveira Brito, Ana Paula Silva Azevedo dos Santos, Ana Gisélia Nascimento, Ana Paula Silva Azevedo dos Santos, Ana Gisélia Nascimento, Fernanda Ferreira Lopes

2022

Abstract

Penile cancer is a rare tumor that accounts for 2% of cancer cases in men in Brazil. Histopathological analyzes are commonly used in its diagnosis, making it possible to assess the degree of the disease, its evolution, and its nature. About a decade ago, scientific works in the field of deep learning were developed to help pathologists make decisions quickly and reliably, opening up possibilities for new contributions to improve such a complex and time-consuming activity for these professionals. In this work, we present the development of a method that uses a DenseNet to diagnose penile cancer in histopathological images, and the construction of a dataset (via the Legal Amazon Penis Cancer Project) used to validate this method. In the experiments performed, an F1-Score of up to 97.39% and a sensitivity of up to 98.33% were achieved in this binary classification problem (normal or squamous cell carcinoma).

Paper Citation

in Harvard Style

Lauande M., Teles A., Lima da Silva L., Matos C., Braz Júnior G., Cardoso de Paiva A., Sousa de Almeida J., Oliveira R., Brito H., Nascimento A., Pestana A., Pestana A., Santos A. and Lopes F. (2022). Classification of Histopathological Images of Penile Cancer using DenseNet and Transfer Learning. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 976-983. DOI: 10.5220/0010893500003124

in Bibtex Style

@conference{visapp22,
author={Marcos Gabriel Mendes Lauande and Amanda Mara Teles and Leandro Lima da Silva and Caio Eduardo Falcão Matos and Geraldo Braz Júnior and Anselmo Cardoso de Paiva and João Dallyson Sousa de Almeida and Rui Miguel Gil da Costa Oliveira and Haissa Oliveira Brito and Ana Paula Silva Azevedo dos Nascimento and Ana Gisélia Pestana and Ana Paula Silva Azevedo dos Pestana and Ana Gisélia Santos and Fernanda Ferreira Lopes},
title={Classification of Histopathological Images of Penile Cancer using DenseNet and Transfer Learning},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={976-983},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010893500003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Classification of Histopathological Images of Penile Cancer using DenseNet and Transfer Learning
SN - 978-989-758-555-5
AU - Lauande M.
AU - Teles A.
AU - Lima da Silva L.
AU - Matos C.
AU - Braz Júnior G.
AU - Cardoso de Paiva A.
AU - Sousa de Almeida J.
AU - Oliveira R.
AU - Brito H.
AU - Nascimento A.
AU - Pestana A.
AU - Pestana A.
AU - Santos A.
AU - Lopes F.
PY - 2022
SP - 976
EP - 983
DO - 10.5220/0010893500003124
PB - SciTePress