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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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 26194 - 11
Main Authors:	Alzakari, Sarah A., Hassairi, Salima, Hussan, Amel Ali Al, Ejbali, Ridha
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 19.07.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 692/699/67 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
Online Access:	Get full text
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