Systematic review of computing approaches for breast cancer detection based computer aided diagnosis using mammogram images
DA Zebari, DA Ibrahim, DQ Zeebaree… - Applied Artificial …, 2021 - Taylor & Francis
Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
[HTML][HTML] A survey of convolutional neural network in breast cancer
Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an
early stage, it could give patients more treatment options and improve the treatment effect …
early stage, it could give patients more treatment options and improve the treatment effect …
Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion
After lung cancer, breast cancer is the second leading cause of death in women. If breast
cancer is detected early, mortality rates in women can be reduced. Because manual breast …
cancer is detected early, mortality rates in women can be reduced. Because manual breast …
TTCNN: A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages
S Maqsood, R Damaševičius, R Maskeliūnas - Applied Sciences, 2022 - mdpi.com
Breast cancer is a major research area in the medical image analysis field; it is a dangerous
disease and a major cause of death among women. Early and accurate diagnosis of breast …
disease and a major cause of death among women. Early and accurate diagnosis of breast …
Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm
According to the American Cancer Society, breast cancer is the second largest cause of
mortality among women after lung cancer. Women's death rates can be decreased if breast …
mortality among women after lung cancer. Women's death rates can be decreased if breast …
AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images
Various deep learning methods have been proposed to segment breast lesions from
ultrasound images. However, similar intensity distributions, variable tumor morphologies …
ultrasound images. However, similar intensity distributions, variable tumor morphologies …
COVID-19 case recognition from chest CT images by deep learning, entropy-controlled firefly optimization, and parallel feature fusion
In healthcare, a multitude of data is collected from medical sensors and devices, such as X-
ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that …
ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that …
Breast cancer detection using mammogram images with improved multi-fractal dimension approach and feature fusion
DA Zebari, DA Ibrahim, DQ Zeebaree… - Applied Sciences, 2021 - mdpi.com
Breast cancer detection using mammogram images at an early stage is an important step in
disease diagnostics. We propose a new method for the classification of benign or malignant …
disease diagnostics. We propose a new method for the classification of benign or malignant …
An optimized framework for breast cancer classification using machine learning
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies
have shown that a larger number of ultrasound images are generated every day, and the …
have shown that a larger number of ultrasound images are generated every day, and the …
A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images
Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying
Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and …
Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and …