Document Type
Article
Source of Publication
Healthcare Analytics
Publication Date
12-1-2025
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution of breast histopathology images. In this paper, breast tumour areas of interest are segmented utilising Attention-Guided Deep Atrous-Residual U-Net at the segmentation stage. Subsequently, patches are processed to form feature vectors VGG19 and ResNet50 for the extraction of deep features from the patches. Also, to fine-tune these models even further, the breast cancer datasets are employed, and Levy Flight-based Red Fox Optimisation is used to extract features from the pre-trained models without further training. The Efficient Capsule Network is used to improve the feature representation and classification capabilities. AGDATUNet-LFRFO-ECN, which was suggested in the study, performed better than other models when tested on the WBCD dataset, with a sensitivity of 99.17 %, specificity of 99.08 %, and accuracy of 99.23 %. What's more, the AGDATUNet-LFRFO-ECN outperformed the available models on BreakHis with a sensitivity of 99.81 %, a specificity of 99.79 %, and an accuracy of 99.82 %, which are the state-of-the-art.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
8
Disciplines
Computer Sciences
Keywords
Breast cancer detection, Deep learning models, Feature extraction methods, Healthcare data analytics, Histology image analytics, Tumour segmentation
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Abugabah, Ahed, "A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification" (2025). All Works. 7496.
https://zuscholars.zu.ac.ae/works/7496
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series