Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
[HTML][HTML] Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward
The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …
networks has significantly progressed and advanced the field of computer vision (CV) and …
Going deep in medical image analysis: concepts, methods, challenges, and future directions
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …
technology has recently attracted so much interest of the Medical Imaging Community that it …
[HTML][HTML] Breast cancer detection and diagnosis using mammographic data: Systematic review
Background Machine learning (ML) has become a vital part of medical imaging research.
ML methods have evolved over the years from manual seeded inputs to automatic …
ML methods have evolved over the years from manual seeded inputs to automatic …
Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network
Mammogram inspection in search of breast tumors is a tough assignment that radiologists
must carry out frequently. Therefore, image analysis methods are needed for the detection …
must carry out frequently. Therefore, image analysis methods are needed for the detection …
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 …
Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet
The counting and identification of white blood cells (WBCs, ie, leukocytes) in blood smear
images play a crucial role in the diagnosis of certain diseases, including leukemia …
images play a crucial role in the diagnosis of certain diseases, including leukemia …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework
Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task
because of many sources of uncertainty, such as speckle noise, very low signal-to-noise …
because of many sources of uncertainty, such as speckle noise, very low signal-to-noise …