Redes neurais convolucionais para a classificação de nódulos tireoidianos através de ultrassonografia
Resumo
A detecção precoce de linfonodos malignos é crítica para o tratamento do câncer de tireoide. Neste estudo, um sistema de diagnóstico é proposto para classificar nódulos malignos com base em imagens de ultrassom, bem como na escala do Thyroid Imaging Reporting and Data System (TI-RADS). Os experimentos implementam 5 redes convolucionais e 3 máquinas de vetores de suporte aplicadas a um conjunto de dados público. Os resultados preliminares indicam o MobileNet como o melhor classificador binário com 89% de acurácia e o DenseNet121 com 56% de acurácia para as 4 categorias TI-RADS.
Referências
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M. Stib and I. Pan. Thyroid nodule malignancy risk stratification using a convolutional neural network. Ultrasound Q., 36(2), 2020.
J. Ding, H. Cheng, C. Ning, J. Huang, and Y. Zhang. Quantitative measurement for thyroid cancer characterization based on elastography. J Ultrasound Med, 30(9):1259–66, 2011.
B. Gopinath and N. Shanthi. Support vector machine based diagnostic system for thyroid cancer using statistical texture features. Asian Pac J Cancer Prev, 14(1):97–102, 2013.
S. Guth, U. Theune, J. Aberle, A. Galach, and C. Bamberger. Very high prevalence of thyroid nodules detected by high frequency (13 mhz) ultrasound examination. Eur J Clin Invest, 39(8):699–706, 2009.
Y. Hang. Thyroid nodule classification in ultrasound images by fusion of conventional features and res-gan deep features. Journal of Healthcare Engineering, 22:9917538, 2021.
J. A. John and N. R. Draper. An alternative family of transformations. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29(2):190–197, 1980.
J. Kwak, K. Han, and J. Yoon. Thyroid imaging reporting and data system for us features of nodules: a step in establishing better stratification of cancer risk. Radiology, 260(3): 892–9, 2011.
W. Li, P. Cao, D. Zhao, and J. Wang. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Methods Med, 216, 2016.
R. Lingam, M. Qarib, and N. Tolley. Evaluating thyroid nodules: predicting and selecting malignant nodules for fine-needle aspiration (fna) cytology. Insights Imaging, 4:617–624, 2013.
O. Moussa, H. Khachnaoui, R. Guetari, and N. Khlifa. Thyroid nodules classification and diagnosis in ultrasound images using fine-tuning deep convolutional neural network. Int J Imaging Syst Technol, 30(1):185–195, 2020.
V. Park, K. Han, and Y. Seong. Diagnosis of thyroid nodules: Performance of a deep learning convolutional neural network model vs. radiologists. Scientific Reports, 9, 2019.
L. Pedraza. An open access thyroid ultrasound-image database. Proceedings of the SPIE, 9287, 2015.
F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte. Breast cancer histopathological image classification using convolutional neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 2560–2567, 2016.
M. Stib and I. Pan. Thyroid nodule malignancy risk stratification using a convolutional neural network. Ultrasound Q., 36(2), 2020.
Publicado
27/06/2023
Como Citar
SEIXAS, Igor Machado; MACHADO, Alexei Manso Correa.
Redes neurais convolucionais para a classificação de nódulos tireoidianos através de ultrassonografia. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 485-490.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.229645.
