Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach

As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse coding, and wavelet networks through a unique architect...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 26194 - 11
Hauptverfasser: Alzakari, Sarah A., Hassairi, Salima, Hussan, Amel Ali Al, Ejbali, Ridha
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.07.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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Accuracy
Algorithms
Autoencoder
Autoencoders
Automation
Breakhis
Breast cancer
Breast Cancer Classification
Breast Neoplasms - classification
Breast Neoplasms - diagnosis
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - pathology
Cancer research
Classification
Datasets
Deep Learning
Female
Histopathology
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Machine learning
Mammography
Mammography - methods
Medical research
Medical screening
multidisciplinary
Science
Science (multidisciplinary)
Sparse coding
Tissues
Tumors
Wavelet Analysis
Wavelet Networks
Wavelet transforms
Wavelet Networks
Autoencoders
Breakhis
2D Image Analysis
Computational Efficiency
Sparse coding
Breast Cancer Classification
Deep Learning
ISSN:2045-2322, 2045-2322
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Zusammenfassung:As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse coding, and wavelet networks through a unique architecture we call the Deep Sparse Wavelet Autoencoder (DSWAE). The key innovation of our method lies in its construction: DSWAE combines stacked wavelet autoencoders to create a robust model specifically designed for differentiating between distinct categories in 2D breast cancer image datasets. This architecture not only enhances classification accuracy but also optimizes computational efficiency by utilizing deep networks with minimal parameters, which significantly reduces processing time and costs. Our experimental results demonstrate the superior performance of the DSWAE model, achieving precision rates of 94.5% for benign and 93.8% for malignant cases, with recall rates of 93.65% for benign and 96.2% for malignant cases. Remarkably, our method attained a perfect precision rate of 100% for normal cases. These results highlight the effectiveness of our approach, which outperforms current state-of-the-art methods in 2D breast cancer image classification.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-11816-y