Classificação de Tumores Cerebrais em Imagens de Ressonância Magnética
Resumo
O câncer de cérebro é caracterizado pelo desenvolvimento de tumores cerebrais malignos. Dessa forma, a detecção precoce é crucial para a sobrevivência dos pacientes. Os avanços em Inteligência Artificial (IA) têm aprimorado a análise de imagens médicas. Entretanto, a classificação de tumores cerebrais ainda é uma tarefa desafiadora. Neste estudo, é utilizada a técnica de transfer learning para classificar os tipos de tumores cerebrais em Meningioma, Glioma, Hipofisário e casos sem tumor, a partir de imagens de ressonância magnética. Para isso, são empregadas as arquiteturas AlexNet, DenseNet201, EfficientNetB7, MobileNetV2 e ResNet50. A rede EfficientNetB7 obteve os resultados mais promissores, alcançando 97,68% para a acurácia de teste.Referências
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Srivastava, A., Khare, A., and Kushwaha, A. (2023). Brain tumor classification using deep learning framework. In 2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC), pages 1–4.
Tan, M. and Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International conference on machine learning, pages 6105–6114. PMLR.
Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pages 839–846.
Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In Graphics gems IV, pages 474–485.
Asif, S., Yi, W., Ain, Q. U., Hou, J., Yi, T., and Si, J. (2022). Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from mr images. IEEE Access, 10:34716–34730.
Bhuvaji, S., Kadam, A., Bhumkar, P., Dedge, S., and Kanchan, S. (2020). Brain Tumor Classification (MRI) [Dataset]. Kaggle. [link]. Acesso em 01 de março de 2024.
Bindu, J. H. and Devi, M. U. (2024). Classification of Brain Tumor Images using Segmentation and Transfer Learning. In 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), pages 225–232.
Bow, S.-T. (2002). Pattern Recognition and Image Preprocessing. Marcel Dekker, Inc., USA, 2nd edition.
Brown, M. B. and Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American statistical association, 69(346):364–367.
Chakrabarty, N. (2017). Brain MRI Images for Brain Tumor Detection [Dataset]. Kaggle. [link]. Acesso em 01 de março de 2024.
Cheng, J. (2017). Brain Tumor Dataset. [link]. Acesso em 01 de março de 2024.
Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., and Haworth, A. (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5):545–563.
El-Assiouti, O. S., Hamed, G., El-Saadawy, H., Ebied, H. M., and Khattab, D. (2023). Regioninpaint, cutoff and regionmix: Introducing novel augmentation techniques for enhancing the generalization of brain tumor identification. IEEE Access, 11:83232–83250.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, 32(200):675–701.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2261–2269.
Instituto Nacional de Câncer (INCA) (2022). Câncer do sistema nervoso central. [link]. Acesso em 01 de março de 2024.
Islam, M. A., Noshin, S. A., Islam, M. R., Razy, M. F., Antara, S., Reza, M. T., and Parvez, M. Z. (2023). A low parametric cnn based solution to efficiently detect brain tumor cells from ultrasound scans. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), pages 1152–1158.
Kaya, Y. and Gürsoy, E. (2023). A mobilenet-based cnn model with a novel fine-tuning mechanism for covid-19 infection detection. Soft Computing, 27(9):5521–5535.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Nemenyi, P. B. (1963). Distribution-free multiple comparisons. Princeton University.
Ottom, M. A., Rahman, H. A., and Dinov, I. D. (2022). Znet: Deep learning approach for 2d mri brain tumor segmentation. IEEE Journal of Translational Engineering in Health and Medicine, 10:1–8.
Padmapriya, S. and Devi, M. G. (2024). Computer-Aided Diagnostic System for Brain Tumor Classification using Explainable AI. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), volume 2, pages 1–6.
Patare, Y., Yadav, S., Pingale, R., and S, K. (2024). Brain Tumor Detection Using Deep Learning(CNN). In 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), pages 1–5.
Raj, J. R. F., Vijayalakshmi, K., Priya, S. K., and Appathurai, A. (2024). Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm. Signal, Image and Video Processing, 18(2):1793–1802.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520.
Shapiro, S. S. and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4):591–611.
Srivastava, A., Khare, A., and Kushwaha, A. (2023). Brain tumor classification using deep learning framework. In 2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC), pages 1–4.
Tan, M. and Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International conference on machine learning, pages 6105–6114. PMLR.
Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pages 839–846.
Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In Graphics gems IV, pages 474–485.
Publicado
25/06/2024
Como Citar
MOREIRA, Andressa G.; SANTOS, Stefane A. dos; OLIVEIRA, Michele F. de; PAULA JÚNIOR, Iális C. de; ASSIS, Débora F. de.
Classificação de Tumores Cerebrais em Imagens de Ressonância Magnética. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 424-435.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2396.
