EfficientEnsemble: Breast cancer diagnosis in ultrasound images using image processing and an Ensemble of EfficientNets
Abstract
Breast cancer diagnostic through ultrasound is challenging due to image complexity and variation in characteristics. The aim of this work is to propose a method that combines preprocessing, balanced data augmentation, and an Ensemble of EfficientNet to enhance diagnostic accuracy. The results show robust validation metrics, achieving an accuracy of 96.67%, specificity of 97.67%, sensitivity of 94.12%, F1-score of 94.96%, and AUC-ROC of 0.95896. The proposed approach could be a valuable tool to aid in breast cancer diagnosis from ultrasound images, potentially enhancing treatment efficiency and improving clinical outcomes.References
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Santos, P., Brito, V., Filho, A. C., Sousa, A., Diniz, J., and Luz, D. (2023). Efficientbacillus: uma arquitetura profunda para detecção dos bacilos de koch. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 198–209, Porto Alegre, RS, Brasil. SBC.
Shan, J., Alam, S. K., Garra, B., Zhang, Y., and Ahmed, T. (2016). Computer-aided diagnosis for breast ultrasound using computerized bi-rads features and machine learning methods. Ultrasound in medicine & biology, 42(4):980–988.
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Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., Jin, P., and Zhuang, S. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer methods and programs in biomedicine, 208:106221.
Alpaydin, E. (2020). Introduction to machine learning. MIT press.
Cao, Z., Yang, G., Chen, Q., Chen, X., and Lv, F. (2020). Breast tumor classification through learning from noisy labeled ultrasound images. Medical Physics, 47(3):1048–1057.
Cheng, H.-D., Shan, J., Ju, W., Guo, Y., and Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey. Pattern recognition, 43(1):299–317.
Chougrad, H., Zouaki, H., and Alheyane, O. (2018). Deep convolutional neural networks for breast cancer screening. Computer methods and programs in biomedicine, 157:19–30.
Diniz, J., Quintanilha, D., Filho, A. C., Jr, D. G., Silva, A., Jr, G. B., Paiva, A., and Luz, D. (2023). Detecção de covid-19 em imagens de raio-x de tórax através de seleção automática de pré-processamento e de rede neural convolucional. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 162–173, Porto Alegre, RS, Brasil. SBC.
Diniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., de Paiva, A. C., and Gattass, M. (2018). Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Computer methods and programs in biomedicine, 156:191–207.
Diniz, J. O. B., Ferreira, J. L., Cortes, O. A. C., Silva, A. C., and de Paiva, A. C. (2022a). An automatic approach for heart segmentation in ct scans through image processing techniques and concat-u-net. Expert Systems with Applications, 196:116632.
Diniz, J. O. B., Ferreira, J. L., Diniz, P. H. B., Silva, A. C., and de Paiva, A. C. (2020). Esophagus segmentation from planning ct images using an atlas-based deep learning approach. Computer Methods and Programs in Biomedicine, 197:105685.
Diniz, J. O. B., Ferreira, J. L., Diniz, P. H. B., Silva, A. C., and Paiva, A. C. (2022b). A deep learning method with residual blocks for automatic spinal cord segmentation in planning ct. Biomedical signal processing and control, 71:103074.
Eroğlu, Y., Yildirim, M., and Çinar, A. (2021). Convolutional neural networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mrmr. Computers in biology and medicine, 133:104407.
Figueredo, W., Silva, I., Diniz, J., Silva, A., Paiva, A., Salomão, A., and Oliveira, M. (2023). Abordagem computacional baseada em deep learning para o diagnóstico de endometriose profunda através de imagens de ressonância magnética. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 138–149, Porto Alegre, RS, Brasil. SBC.
Gonzalez, R. and Woods, R. (2008). Digital image processing. Pearson, Prentice Hall.
He, K., Sun, J., and Tang, X. (2012). Guided image filtering. IEEE transactions on pattern analysis and machine intelligence, 35(6):1397–1409.
Júnior, D. A. D., da Cruz, L. B., Diniz, J. O. B., da Silva, G. L. F., Junior, G. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2021). Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost. Expert Systems with Applications, 183:115452.
Júnior, D. D., Cruz, L., Diniz, J., Júnior, G. B., and Silva, A. (2021). Classificação automática de glóbulos brancos usando descritores de forma e textura e extreme gradient boosting. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, pages 95–106, Porto Alegre, RS, Brasil. SBC.
Kılıçarslan, G., Koç, C., Özyurt, F., and Gül, Y. (2023). Breast lesion classification using features fusion and selection of ensemble resnet method. International Journal of Imaging Systems and Technology, 33(5):1779–1795.
Matos, C., Oliveira, M., Diniz, J., Fernandes, A., Junior, G. B., and Paiva, A. (2023). Ppmdeeplab: Módulo de pirâmide de pooling como codificador da rede deeplabv3+ para segmentação de rins, cistos e tumores renais. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 210–221, Porto Alegre, RS, Brasil. SBC.
Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., and Chang, R.-F. (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer methods and programs in biomedicine, 190:105361.
Pang, T., Wong, J. H. D., Ng, W. L., and Chan, C. S. (2021). Semi-supervised ganbased radiomics model for data augmentation in breast ultrasound mass classification. Computer Methods and Programs in Biomedicine, 203:106018.
Santos, P., Brito, V., Filho, A. C., Sousa, A., Diniz, J., and Luz, D. (2023). Efficientbacillus: uma arquitetura profunda para detecção dos bacilos de koch. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 198–209, Porto Alegre, RS, Brasil. SBC.
Shan, J., Alam, S. K., Garra, B., Zhang, Y., and Ahmed, T. (2016). Computer-aided diagnosis for breast ultrasound using computerized bi-rads features and machine learning methods. Ultrasound in medicine & biology, 42(4):980–988.
Sirjani, N., Oghli, M. G., Tarzamni, M. K., Gity, M., Shabanzadeh, A., Ghaderi, P., Shiri, I., Akhavan, A., Faraji, M., and Taghipour, M. (2023). A novel deep learning model for breast lesion classification using ultrasound images: A multicenter data evaluation. Physica Medica, 107:102560.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, page 6105. PMLR.
Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., Jin, P., and Zhuang, S. (2021). Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion. Computer methods and programs in biomedicine, 208:106221.
Published
2024-06-25
How to Cite
DINIZ, João O. B.; DIAS JR, Domingos A.; CRUZ, Luana B. da; MARQUES, Ricardo C. S.; GOMES JR, Daniel L.; CORTÊS, Omar A. C.; CARVALHO FILHO, Antônio O. de; QUINTANILHA, Darlan B. P..
EfficientEnsemble: Breast cancer diagnosis in ultrasound images using image processing and an Ensemble of EfficientNets. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 202-213.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2155.
