An Enhanced Mammogram Classification to Detect Breast Cancer using Boosted PSO Feature Selection and Multi-Feature Analysis

• Published in: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 5
• Main Authors: Kumari, L Kanya, Srinivasu, N, Jagadesh, B N
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.07.2023
• Subjects: Adaptive equalizers; Boosting; contrast limited adaptive histogram equalization; Feature extraction; Histograms; light gradient boosting machine; Machine learning algorithms; modified weighted particle swarm optimization; multi feature analysis; Shape; Solid modeling
• ISSN: 2473-7674
• DOI: 10.1109/ICCCNT56998.2023.10306907

Breast cancer is a globally recognized public health issue, particularly impacting women. While mammogram screening is a widely used method for identifying cancerous cells, accurately distinguishing these cells from the surrounding tissue remains a challenge. A novel approach is presented in this study, to categorize mammograms. Initially, preprocessing is done by median filter with Contrast Limited Adaptive Histogram Equalization (CLAHE). Multi-Feature Analysis (MFA) is used for feature extraction to extract texture, shape, and volume information from the mammograms. A Modified Weighted Particle Swarm Optimization (MWPSO) algorithm selects the optimal features, which are fed to the Light Gradient Boosting Machine (Light GBM) for classification. The model's performance is evaluated through k-fold cross-validation and demonstrates high accuracy in mammogram classification. By combining the strengths of image processing and machine learning algorithms, the proposed methodology provides a powerful tool for mammogram classification.