Classification of Lung Cancer in Medical Imaging Using Convolutional Networks with CLAHE Preprocessing and Wavelet Transform
Resumo
Convolutional neural networks (CNNs) play a crucial role in the early detection of diagnoses, assisting healthcare professionals in decision-making. This study uses different CNNs (AlexNet, ResNet-50, and EfficientNet) to classify computed tomography (CT) scan images for lung cancer detection, using the IQ-QTH/NCC and LIDC-IDRI datasets. The models were trained and evaluated with various magnifications and epochs, measuring the performance of each model based on metrics such as accuracy, precision, and recall. The results showed that ResNet-50 achieved an average accuracy of 96.12%. AlexNet reached 94.38%, and EfficientNet obtained 94.38%.
Palavras-chave:
Lung Cancer, Lung, Convolutional Neural Network, Wavelet, CLAHE
Referências
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M. A. Wani, F. A. Bhat, S. Afzal, and A. I. Khan, Advances in Deep Learning, 1st ed., ser. Studies in Big Data. Springer Singapore, 2020. [Online]. DOI: 10.1007/978-981-13-6794-6
Suji, R.J., Godfrey, W.W. \& Dhar, J. Explorando codificadores pré-treinados para tarefa de segmentação de nódulos pulmonares usando o conjunto de dados LIDC-IDRI. Ferramentas Multimed Appl 83, 9685–9708 (2024). DOI: 10.1007/s11042-023-15871-3
BRASIL. Ministério da Saúde. Instituto Nacional de Câncer - INCA. Cancer pulmon. Brasilia, DF, 2023. Disponível em: [link]. Data de acesso: 09 ago. 2024.
SOCIEDADE BRASILEIRA DE RADIOTERAPIA - SBRT. Fila de espera por exames na capital. 2023. Disponível em: [link]. Acesso em: 09 ago. 2024.
CHIU, H.-Y. et al. Artificial intelligence for early detection of chest nodules in x-ray images. Biomedicines, Basel, v. 10, n. 11, p. 15, 2022.
BRASIL. Ministério da Saúde. comissão nacional de incorporação de tecnologias (CONITEC). Relatório para sociedade: informações sobre recomendações de incorporação de medicamentos e outras tecnologias no SUS: Disponível em: [link]. Acesso em: 09 ago. 2024.
NEOPLASIA: O que é, sintomas e diferença entre maligno ou benigno. Tua saúde, abril de 2023. Disponível em: [link]. Acesso em: 09 ago. 2024
KROHN, J. Deep learning illustrated. London: AddisonWesley, 2020.
ARIF, T. M.; RAHIM, A. Deep Learning for Engineers. Boca Raton: CRC, 2024.
X. Wang, Y. Zhao e F. Pourpanah, “Recent advances in deep learning,” International Journal of Machine Learning and Cybernetics, vol. 11, p. 747–750, 2020.
KELLEHER, J. D. Deep learning. Cambridge: The MIT Press, 2019.
M. A. Wani, F. A. Bhat, S. Afzal, and A. I. Khan, Advances in DeepLearning, 1st ed., ser. Studies in Big Data. Springer Singapore, 2020. [Online]. DOI: 10.1007/978-981-13-6794-6
Alyasriy, Hamdalla (2020), “The IQ-OTHNCCD lung cancer dataset”, Mendeley Data, V1, DOI: 10.17632/bhmdr45bh2.1
Armato, S.G., III, McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., van Beek, E.J.R., Yankelevitz, D., Biancardi, A.M., Bland, P.H., Brown, M.S., Engelmann, R.M., Laderach, G.E., Max, D., Pais, R.C., Qing, D.P.-.-Y., Roberts, R.Y., Smith, A.R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G.W., Jude, C.M., Munden, R.F., Petkovska, I., Quint, L.E., Schwartz, L.H., Sundaram, B., Dodd, L.E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Vande Casteele, A., Gupte, S., Sallam, M., Heath, M.D., Kuhn, M.H., Dharaiya, E., Burns, R., Fryd, D.S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B.Y. and Clarke, L.P. (2011), The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans. Med. Phys., 38: 915-931. DOI: 10.1118/1.3528204
S. Pizer, R. Johnston, J. Ericksen, B. Yankaskas, and K. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,” in [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, pp. 337–345.
F. Rodrigues, F. Patrício, E. Oliveira, and A. Paula, Desafios do manejo no controle de doenças do café- F I TOSSA N I DA D E, 03 2019, pp. 52–54.
BELCIUG, S. Artificial intelligence in cancer: diagnostic to tailored treatment. Cambridge: Academic, 2020.
H. F. Al-Yasriy, M. S. AL-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, "Diagnosis of Lung Cancer Based on CT Scans Using CNN," *IOP Conference Series: Materials Science and Engineering*, vol. 928, no. 2, pp. 022035, Nov. 2020. DOI: 10.1088/1757-899X/928/2/022035
Narin, D., & Onur, T. Ö. (2022). The Effect of Hyper Parameters on the Classification of Lung Cancer Images Using Deep Learning Methods. Erzincan University Journal of Science and Technology, 15(1), 258-268. DOI: 10.18185/erzifbed.1006560
M. A. Wani, F. A. Bhat, S. Afzal, and A. I. Khan, Advances in Deep Learning, 1st ed., ser. Studies in Big Data. Springer Singapore, 2020. [Online]. DOI: 10.1007/978-981-13-6794-6
Suji, R.J., Godfrey, W.W. \& Dhar, J. Explorando codificadores pré-treinados para tarefa de segmentação de nódulos pulmonares usando o conjunto de dados LIDC-IDRI. Ferramentas Multimed Appl 83, 9685–9708 (2024). DOI: 10.1007/s11042-023-15871-3
Publicado
06/11/2024
Como Citar
RIBEIRO, Moisés José M.; SOUSA, Pedro Moises de.
Classification of Lung Cancer in Medical Imaging Using Convolutional Networks with CLAHE Preprocessing and Wavelet Transform. In: WORKSHOP DE SISTEMAS DE INFORMAÇÃO (WSIS), 15. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
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p. 45-50.
DOI: https://doi.org/10.5753/wsis.2024.33671.
