Transfer learning in breast mammogram abnormalities classification with mobilenet and nasnet

LG Falconí, M Pérez, WG Aguilar - … international conference on …, 2019 - ieeexplore.ieee.org
Breast cancer has an important incidence in women mortality worldwide. Currently,
mammography is considered the gold standard for breast abnormalities screening …

[PDF][PDF] Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database

LG Falconi, M Perez, WG Aguilar… - Adv. Sci. Technol. Eng …, 2020 - academia.edu
Breast cancer has an important incidence in women mortality worldwide. Currently,
mammography is considered the gold standard for breast abnormalities screening …

Breast cancer detection and localization using mobilenet based transfer learning for mammograms

W Ansar, AR Shahid, B Raza, AH Dar - … Arab Emirates, March 18–19, 2020 …, 2020 - Springer
Breast cancer is the major cause of death among women. The best and most efficient
approach for controlling cancer progression is early detection and diagnosis. As opposed to …

[HTML][HTML] Feature learning based on connectivity estimation for unbiased mammography mass classification

G Li, R Zwiggelaar - Computer Vision and Image Understanding, 2024 - Elsevier
Breast cancer is the most commonly diagnosed female malignancy worldwide. Recent
developments in deep convolutional neural networks have shown promising performance …

Distributed rough set based feature selection approach to analyse deep and hand-crafted features for mammography mass classification

A Hamidinekoo, ZC Dagdia, Z Suhail… - … Conference on Big …, 2018 - ieeexplore.ieee.org
Breast cancer has a high incidence among women worldwide. This, together with the recent
developments in deep learning based convolutional networks, have motivated research …

Automated mammogram analysis with a deep learning pipeline

A Hamidinekoo, E Denton, R Zwiggelaar - arXiv preprint arXiv:1907.11953, 2019 - arxiv.org
Current deep learning based detection models tackle detection and segmentation tasks by
casting them to pixel or patch-wise classification. To automate the initial mass lesion …

Pre-biopsy multi-class classification of breast lesion pathology in mammograms

T Tlusty, M Ozery-Flato, V Barros, E Barkan… - Machine Learning in …, 2021 - Springer
Abstract Characterization of lesions by artificial intelligence (AI) has been the subject of
extensive research. In recent years, many studies demonstrated the ability of convolution …

Breast Cancer Screening Using Deep Learning

A La Cruz, CAD Santacruz, L Polo… - 2022 IEEE Sixth …, 2022 - ieeexplore.ieee.org
Breast cancer is a disease in which the cells lining the ducts or lobules of the breast
glandular tissue begin to grow out of control. Early detection increases the probability of …

An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms

A Hamidinekoo, E Denton, K Honnor… - … Workshop on Breast …, 2020 - spiedigitallibrary.org
Early diagnosis of breast cancer can increase survival rate. The assessment process for
breast screening follows a triple assessment model: appropriate imaging, clinical …

An Interpretable Cnn-Based Model for Mass Classification in Mammography

G Li, M Zhou, Y Fu, N Alam, E Denton… - Available at SSRN … - papers.ssrn.com
Mammography is the primary screening method for lesion visualisation and detecting early
potentially cancerous changes in breast tissue. The application of deep learning based …