A review on the use of deep learning for medical images segmentation
M Aljabri, M AlGhamdi - Neurocomputing, 2022 - Elsevier
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …
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 …
Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images
Segmentation of the breast region and pectoral muscle are fundamental subsequent steps
in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region …
in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region …
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …
regions of interest. Because of these differences, neural network architectures that work well …
Preprocessing of breast cancer images to create datasets for deep-CNN
Breast cancer is the most diagnosed cancer in Australia with crude incidence rates
increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 …
increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 …
Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey
Advances in deep learning networks, especially deep convolutional neural networks
(DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These …
(DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These …
Arf-net: An adaptive receptive field network for breast mass segmentation in whole mammograms and ultrasound images
UNet adopting an encoder-decoder structure has been used widely in medical image
segmentation tasks for its outstanding performance. However, in our work, we find that UNet …
segmentation tasks for its outstanding performance. However, in our work, we find that UNet …
A comprehensive review on multi-organs tumor detection based on machine learning
Tumor is comprised of abnormally growing regions that is dangerous for human survival.
Therefore, early stage tumor detection is useful for increase of survival rate although it is …
Therefore, early stage tumor detection is useful for increase of survival rate although it is …
Application of deep learning in histopathology images of breast cancer: a review
With the development of artificial intelligence technology and computer hardware functions,
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
Deep learning-based medical images segmentation of musculoskeletal anatomical structures: a survey of bottlenecks and strategies
L Bonaldi, A Pretto, C Pirri, F Uccheddu, CG Fontanella… - Bioengineering, 2023 - mdpi.com
By leveraging the recent development of artificial intelligence algorithms, several medical
sectors have benefited from using automatic segmentation tools from bioimaging to segment …
sectors have benefited from using automatic segmentation tools from bioimaging to segment …