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Improved Breast Cancer Detection in Mammography Images: Integration of Convolutional Neural Network and Local Binary Pattern Approach
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Author(s): Olalekan Joel Awujoola (Nigerian Defence Academy, Nigeria), Theophilus Enem Aniemeka (Nigerian Airforce Institute of Technology, Nigeria), Francisca N. Ogwueleka (University of Abuja, Nigeria), Oluwasegun Abiodun Abioye (Nigerian Defence Academy, Nigeria), Abidemi Elizabeth Awujoola (Nigerian Defence Academy, Nigeria)and Celestine Ozoemenam Uwa (Nigerian Defence Academy, Nigeria)
Copyright: 2024
Pages: 28
Source title:
Machine Learning Algorithms Using Scikit and TensorFlow Environments
Source Author(s)/Editor(s): Puvvadi Baby Maruthi (Dayananda Sagar University, India), Smrity Prasad (Dayananda Sagar University, India)and Amit Kumar Tyagi ( National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-8531-6.ch011
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Abstract
Cancer, characterized by uncontrolled cell division, is an incurable ailment, with breast cancer being the most prevalent form globally. Early detection remains critical in reducing mortality rates. Medical imaging is vital for localizing and diagnosing breast cancer, providing key insights for identification. This study introduces an automatic hybrid feature recognition method for breast cancer diagnosis using images from two mammography datasets. The method employs a convolutional neural network (CNN) and local binary pattern (LBP) for feature extraction. Correlation-based feature selection techniques reduce dimensionality, enabling faster computation and improved accuracy. The proposed model's superiority is established through comparative analysis with cutting-edge deep models, achieving 96% accuracy across the MIAS and INbreast datasets. The hybrid method demonstrates high accuracy with minimal computational tasks.
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