Open Access

ARTICLE

A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

1 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Mohammad Mehedi Hassan. Email:

(This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)

Computer Modeling in Engineering & Sciences 2024, 141(3), 2575-2608. https://doi.org/10.32604/cmes.2024.055011

Received 13 June 2024; Accepted 11 September 2024; Issue published 31 October 2024

Abstract

Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a broad range of features from the Medical Image Analysis Society (MIAS) mammography dataset. Subsequently, a GA optimizes these features by selecting the most relevant and least redundant, aiming to overcome the typical pitfalls of high dimensionality. The selected features are then utilized to train several classifiers, including Linear and Polynomial Support Vector Machines (SVMs), K-Nearest Neighbors, Decision Trees, and Random Forests, enabling a robust evaluation of the method’s effectiveness across varied learning algorithms. Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy, from 83.33% to 89.58%, underscoring the method’s efficacy. By detailing these steps, we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging.

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Keywords

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