Detección de cáncer de mama mediante imágenes con clasificador híbrido
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1353Keywords:
Image Classification, Breast Cancer, Hybrid LearningAbstract
Objectives: Develop Machine Learning (ML) algorithms for accurate classification of ultrasound images to support the diagnosis of breast cancer. Method: Implementation of a new hybrid learning model that combines the techniques of LightGBM, Multilayer Perceptron Network (MLP), Support Vector Machine (SVM) and Relativistic Particle Swarm weight optimization (RPSO). Results: The classifier model obtained resulted in an accuracy of 98% on the test data, therefore offering high accuracy. Conclusion: The proposed model obtained results superior to those of works found in the literature, making it a promising diagnostic support tool.
References
INCA. (2022). Instituto Nacional de Câncer. Recuperado de: <https://www.gov.br/inca/pt-br/assuntos/cancer/numeros/>. Acessado em 18 mar 2023.
Chamorro HM, Colturato PL, Fattori NCM. "Câncer de mama: fatores de risco e a importância da detecção precoce." Revista Científica Eletrônica de Ciências Aplicadas. 2021; 1 (1).
Raza A, et al. "DeepBreastCancerNet: A novel deep learning model for breast cancer detection using ultrasound images." Applied Sciences. 2023; 13(4): 2082. DOI: https://doi.org/10.3390/app13042082
Abdul Halim AA, Andrew AM, Mohd Yasin MN, Abd Rahman MA, Jusoh M, Veeraperumal V, Rahim HA, Illahi U, Abdul Karim MK, Scavino E. Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review. Applied Sciences. 2021; 11(22):10753. DOI: https://doi.org/10.3390/app112210753
Rehman MZU, et al. "An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model." Computer Systems Science and Engineering. 2023; 46(1). DOI: https://doi.org/10.32604/csse.2023.035382
Joshi RC et al. "An efficient deep neural network based abnormality detection and multi-class breast tumor classification." Multimedia Tools and Applications. 2022; 81(10). DOI: https://doi.org/10.1007/s11042-021-11240-0
Almutairi SM et al. "An efficient USE-Net deep learning model for cancer detection." International Journal of Intelligent Systems 2023.. DOI: https://doi.org/10.1155/2023/8509433
Manzari ON et al. "MedViT: a robust vision transformer for generalized medical image classification." Computers in Biology and Medicine. 2023; 157: 106791. DOI: https://doi.org/10.1016/j.compbiomed.2023.106791
Yahata E, Winnikow EP, Suyama R, Simões PW. Explicabilidade em Modelos Preditivos de Machine Learning no Câncer de Mama. Journal Health Informatics. 2023 jul; 15(1): 1-14. DOI: https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1090
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Anais do 31st Conference on Neural Information Processing Systems. 2017; 3146-3154.
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986; 323(1): 533–536. DOI: https://doi.org/10.1038/323533a0
Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20(3): 273-297. DOI: https://doi.org/10.1007/BF00994018
Roder M et al. "Harnessing particle swarm optimization through relativistic velocity." 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE. 2020. DOI: https://doi.org/10.1109/CEC48606.2020.9185752
Procópio, Fábio. "Otimização por enxames de partículas: usando uma adaptação de serendipidade". 2018.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Duchesnay MPE. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12: 2825-2830.
Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T. Scikit-image: image processing in Python. PeerJ. 2014; 2: 2-18. DOI: https://doi.org/10.7717/peerj.453
Virtanen P, Gommers R, Oliphant T.E et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods. 2020; 17: 261-272. DOI: https://doi.org/10.1038/s41592-019-0686-2
McKinney W. Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference (SciPy 2010). 2010; 56-61. DOI: https://doi.org/10.25080/Majora-92bf1922-00a
Harris CR, Millman KJ, Walt SJ, Gommers R, Virtanen P, Cournapeau D et al. Array programming with NumPy. Nature. 2020;585: 357-362. DOI: https://doi.org/10.1038/s41586-020-2649-2
Lemenkova P. Python Libraries Matplotlib, Seaborn and Pandas for Visualization Geo-spatial Datasets Generated by QGIS. Anais do Universitatii "Alexandru Ioan Cuza" din Iasi - seria Geografie. 2020; 64(1): 13-32.
Rosa GH, Rodrigues D, Papa JP. Opytimizer: A Nature-Inspired Python Optimizer. Universidade de São Paulo. 2020.
Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020; 28: 104863. DOI: https://doi.org/10.1016/j.dib.2019.104863
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