Classificação e Recuperação de Imagens Baseada em Conteúdo Aplicada a Imagens de Tomografia do Tórax
Abstract
The diagnosis and treatment of pulmonary nodules can significantly improve the patient’s survival rate. For a lung cancer examination, patients are submitted to X-ray, CT or MRI scans to differentiate the development of lung abnormalities, enabling the use of computers for autonomous classification. In this work we performed performance tests between methods based on Convolutional Neural Networks previously trained with the ImageNet image base for the extraction of pulmonary nodule attributes. Features were classified using content-based image retrieval models.
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Breiman, L. (2001b). Random forests. Mach. Learn., 45(1):5–32.
Chollet, F. (2016). Xception: Deep learning with depthwise separable convolutions.
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Gudivada, V. N. and Raghavan, V. V. (1995). Content based image retrieval systems. Computer, 28(9):18–22.
Huang, G., Liu, Z., and Weinberger, K. Q. (2016). Densely connected convolutional networks. CoRR, abs/1608.06993.
Hussain, M., Bird, J. J., and Faria, D. R. (2018). A study on cnn transfer learning for image classification. In UK Workshop on Computational Intelligence, pages 191–202. Springer.
INCA (2019). Câncer de pulmão.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
Nishio, M., Sugiyama, O., Yakami, M., Ueno, S., Kubo, T., Kuroda, T., and Togashi, K. (2018). Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PloS one, 13(7):e0200721.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Ioffe, S., and Vanhoucke, V. (2016). Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR, abs/1602.07261.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567.
Tajbakhsh, N. and Suzuki, K. (2017). Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: Mtanns vs. cnns. Pattern recognition, 63:476–486.
Yuan, J., Liu, X., Hou, F., Qin, H., and Hao, A. (2018). Hybrid-feature-guided lung nodule type classification on ct images. Computers & Graphics, 70:288–299.
Published
2019-12-26
How to Cite
COUTO JÚNIOR, Helio R. V.; V. E SILVA, Romuere R..
Classificação e Recuperação de Imagens Baseada em Conteúdo Aplicada a Imagens de Tomografia do Tórax. In: REGIONAL SCHOOL OF APPLIED COMPUTING FOR HEALTH (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 306-311.
