Abstract: Mammography and ultrasound are the main medical imaging modalities for identifying breast lesions. Computer-assisted diagnosis (CAD) is an important tool for radiologists, helping them differentiate benign and malignant lesions more quickly and objectively. The use of appropriate features in mammography and ultrasound is one of the key factors determining the success of computer-assisted diagnosis (CAD) results for breast cancer systems. The diversity of feature forms and extraction techniques is a challenge. Additionally, the use of a single classification algorithm often causes noise, bias, and is not robust. We propose a convolutional layer-based feature extraction technique in the ensemble learning model for the classification of breast cancer. This study uses 439 mammography images (203 benign, 236 malignant) and 421 ultrasound images (244 benign, 177 malignant). This research consists of several stages, including data pre-processing, feature extraction, classification, and performance evaluation. We used four convolution layer-based feature extraction techniques: simple convolution (SC), feature fusion convolution (FFC), feature fusion depthwise convolution (FFDC), and feature fusion depthwise separable convolution (FFDSC). The model uses five machine learning algorithms (support vector machine, random forest, k nearest neighbours, decision tree, and logistic regression) that are part of ensemble learning. The experimental results show that the use of the FFC convolution layer in ensemble learning has the best performance for both datasets. In the ultrasound data set, the FFC achieved a value of 0.90 in each of the accuracy, precision, recall, specificity, and F1 score metrics. In the mammography data set, the FFC achieved a value of 0.98 on each of the same metrics. These results show the effectiveness of feature fusion in improving classification performance in the soft voting classifier for ensemble learning.

Shofwatul ‘Uyun, Lina Choridah, Slamet Riyadi and Ade Umar Ramadhan, “Convolutional Layer-Based Feature Extraction in an Ensemble Machine Learning Model for Breast Cancer Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151244

@article{‘Uyun2024,
title = {Convolutional Layer-Based Feature Extraction in an Ensemble Machine Learning Model for Breast Cancer Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151244},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151244},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Shofwatul ‘Uyun and Lina Choridah and Slamet Riyadi and Ade Umar Ramadhan}
}