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.
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