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All Journal International Journal of Electrical and Computer Engineering
Razali, Noor Fadzilah
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Computer Science & IT Electrical & Electronics Engineering

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Genetic algorithm-adapted activation function optimization of deep learning framework for breast mass cancer classification in mammogram images Razali, Noor Fadzilah; Isa, Iza Sazanita; Sulaiman, Siti Noraini; Osman, Muhammad Khusairi; Karim, Noor Khairiah A.; Damit, Dayang Suhaida Awang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2820-2833

Abstract

The convolutional neural network (CNN) has been explored for mammogram cancer classification to aid radiologists. CNNs require multiple convolution and non-linearity repetitions to learn data sparsity, but deeper networks often face the vanishing gradient effect, which hinders effective learning. The rectified linear unit (ReLU) activation function activates neurons only when the output exceeds zero, limiting activation and potentially lowering performance. This study proposes an adaptive ReLU based on a genetic algorithm (GA) to determine the optimal threshold for neuron activation, thus improving the restrictive nature of the original ReLU. We compared performances on the INbreast and IPPT-mammo mammogram datasets using ReLU and leakyReLU activation functions. Results show accuracy improvements from 95.0% to 97.01% for INbreast and 84.9% to 87.4% for IPPT-mammo with ReLU and from 93.03% to 99.0% for INbreast and 84.03% to 91.06% for IPPT-mammo with leakyReLU. Significant accuracy improvements were observed for breast cancer classification in mammograms, demonstrating its potential to aid radiologists with more robust and reliable diagnostic tools.
Co-Authors Damit, Dayang Suhaida Awang Isa, Iza Sazanita Karim, Noor Khairiah A. Osman, Muhammad Khusairi Sulaiman, Siti Noraini