Abstract

Breast cancer remains a major global health concern, and although mammography is the standard screening technique, its inherently low contrast often limits diagnostic precision. This study presents a multistage computer-aided diagnosis (CADx) framework for mammographic image enhancement, segmentation, and classification. The pipeline begins with preprocessing, where wavelet-domain denoising and contrast optimization through modified morphological operations are applied to suppress noise and improve visibility. A pulse-coupled neural network (PCNN)—a biologically inspired model of the mammalian visual cortex—is then employed to segment both microcalcifications and masses. For classification, deep features extracted from the “fc8” layer of VGG19 are input to an ensemble learning framework implemented via MATLAB’s fitcensemble function to distinguish between benign and malignant cases. The proposed framework was evaluated on the mammographic image analysis society (MIAS) dataset using 20 images for microcalcification (MC) segmentation, 15 images for various mass types across different background tissues, and 100 images (50 benign, 50 malignant) for classification. Segmentation results for both MCs and masses demonstrated performance comparable to or better than state-of-the-art methods. For classification, Totally Corrective Boosting (TotalBoost) and Linear Programming Boosting (LPBoost) achieved 100% accuracy on both training and test sets, while Adaptive Boosting (AdaBoost) and Bagging attained 100% training accuracy and 94.7% test accuracy. Although tested on a small dataset, the integrated preprocessing and ensemble-based strategy yielded promising outcomes in both segmentation and classification stages.

Keywords

Breast cancer detection, Mammography, Computer-aided diagnosis (CADx), Pulse-coupled neural network (PCNN), Ensemble learning, VGG19 features.

Cite this article

Hua YT, Nguyen GH, Dang LV. Detection and classification of breast masses and microcalcifications using a pulse-coupled neural network and ensemble machine learning.International Journal of Advanced Technology and Engineering Exploration.2025;12(131):1-23

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