A hybrid workflow of residual convolutional transformer encoder for breast cancer classification using digital X-ray mammograms
Breast cancer, which attacks the glandular epithelium of the breast, is the second most
common kind of cancer in women after lung cancer, and it affects a significant number of …
common kind of cancer in women after lung cancer, and it affects a significant number of …
ETECADx: Ensemble self-attention transformer encoder for breast cancer diagnosis using full-field digital X-ray breast images
Early detection of breast cancer is an essential procedure to reduce the mortality rate among
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
Efficient breast cancer diagnosis from complex mammographic images using deep convolutional neural network
Medical image analysis places a significant focus on breast cancer, which poses a
significant threat to women's health and contributes to many fatalities. An early and precise …
significant threat to women's health and contributes to many fatalities. An early and precise …
[HTML][HTML] A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images
With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and
mortality rate of this disease pose severe global health issues for women. Identifying the …
mortality rate of this disease pose severe global health issues for women. Identifying the …
A comprehensive survey on deep-learning-based breast cancer diagnosis
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000
deaths from breast cancer were recorded globally in 2020, making it the most common …
deaths from breast cancer were recorded globally in 2020, making it the most common …
An efficient transfer and ensemble learning based computer aided breast abnormality diagnosis system
F Azour, A Boukerche - IEEE Access, 2022 - ieeexplore.ieee.org
Breast cancer is the second most deadly type of cancer globally among women and can be
prevented to a great extent in the case of early detection. In order to raise the survival rate …
prevented to a great extent in the case of early detection. In order to raise the survival rate …
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally.
Breast cancer detection needs accurate mammography interpretation and analysis, which is …
Breast cancer detection needs accurate mammography interpretation and analysis, which is …
Deep feature–based automatic classification of mammograms
Breast cancer has the second highest frequency of death rate among women worldwide.
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
Deep learning computer-aided diagnosis for breast lesion in digital mammogram
For computer-aided diagnosis (CAD), detection, segmentation, and classification from
medical imagery are three key components to efficiently assist physicians for accurate …
medical imagery are three key components to efficiently assist physicians for accurate …
A deep learning computer-aided diagnosis approach for breast cancer
AM Zaalouk, GA Ebrahim, HK Mohamed, HM Hassan… - Bioengineering, 2022 - mdpi.com
Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of
lives and amounts of money. It is the world's leading type of cancer among women and a …
lives and amounts of money. It is the world's leading type of cancer among women and a …