Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

• Published in: المجلة العراقية للعلوم الاحصائية Vol. 21; no. 1; pp. 46 - 58
• Main Authors: Karzan Faidhi Hamad, Bulent Celik, Rizgar Maghded Ahmed
• Format: Journal Article
• Language: Arabic; English
• Published: College of Computer Science and Mathematics, University of Mosul 01-06-2024
• Subjects: artificial neural network linear discriminant analysis medical imaging (mammogram image) roi; image processing
• ISSN: 1680-855X; 2664-2956
• DOI: 10.33899/iqjoss.2024.0183231

In recent years, there has been a notable increase in interest regarding intelligent classification techniques rooted in Machine Learning within the domain of medical science. Specifically, machine learning, a pivotal area of artificial intelligence, has been extensively utilized to aid medical professionals in predicting and diagnosing various diseases. This study applies two distinct machine learning algorithms to address a medical diagnosis concern related to circular masses in breast cancer. The dataset encompasses 150 cases of breast cancer patients. The primary objective is to assess and compare the effectiveness of artificial neural networks (ANNs) and linear discriminant analysis (LDA) classifiers based on key criteria: accuracy, sensitivity, specificity, and the kappa coefficient in predicting circular masses within breast cancer. Results indicate that the performance of the ANN classifier surpasses that of the LDA model, achieving an accuracy of 97.7%, sensitivity of 95%, specificity of 100%, and a kappa coefficient of 95.31%. Additionally, the final fitted models unveil the pivotal factors significantly influencing circular masses in breast cancer, highlighting Solidity and Entropy as the most critical variables.