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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Pujiono
Universitas Dian Nuswantoro
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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A Sustainable Computational Framework for Breast Cancer Screening: Optimizing High-Dimensional Feature Spaces via MVP-PCA for Resource-Constrained Environments Etika kartikadarma; Ahmad Zainul Fanani; Pujiono; Affandy
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3858

Abstract

The rapid integration of Electronic Health Records (EHR) demands the efficient processing of high-resolution medical images. However, deep learning architectures applied to mammography classification often produce massive, high-dimensional feature spaces susceptible to the curse of dimensionality and anatomical noise. Furthermore, conventional dimensionality reduction approaches tend to cause over-reduction, which destroys crucial microcalcification textures. To address these challenges, this study proposes a CPU-efficient hybrid dimensionality reduction framework integrating Mean Vector Projection (MVP) and Principal Component Analysis (PCA) on features extracted by SqueezeNet. The MVP layer acts as a crucial pre-conditioner to stabilize intra-class variance before PCA decomposition. Experimental results demonstrate that the proposed MVP-PCA framework successfully linearizes the feature space and achieves an extreme compression rate of 99.11%, reducing 264,702 features to 2,355 essential components. The peak accuracy reaches 97.58% with a minimal False Negative rate (2 cases), providing a sustainable diagnostic solution for healthcare facilities with limited technological resources.
Co-Authors Affandy Affandy Ahmad Zainul Fanani Etika Kartikadarma