Nabilah Ruza1, Saiful Izzuan Hussain2, Siti Kamariah Che Mohamed3, Mohd Hafiz Arzmi4,5
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.3, pp. 1-7, 2023, DOI:10.23967/j.rimni.2023.08.002
- 01 September 2023
Abstract Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5 percent, has the potential to be the More >
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