[Retracted] U‐Net‐Based Medical Image Segmentation
Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
[Retracted] Deep Neural Networks for Medical Image Segmentation
P Malhotra, S Gupta, D Koundal… - Journal of …, 2022 - Wiley Online Library
Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …
applications in the field of analysis of images, augmented reality, machine vision, and many …
Towards a guideline for evaluation metrics in medical image segmentation
In the last decade, research on artificial intelligence has seen rapid growth with deep
learning models, especially in the field of medical image segmentation. Various studies …
learning models, especially in the field of medical image segmentation. Various studies …
Diffuse Attend and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
Producing quality segmentation masks for images is a fundamental problem in computer
vision. Recent research has explored large-scale supervised training to enable zero-shot …
vision. Recent research has explored large-scale supervised training to enable zero-shot …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Modified U-net architecture for segmentation of skin lesion
Dermoscopy images can be classified more accurately if skin lesions or nodules are
segmented. Because of their fuzzy borders, irregular boundaries, inter-and intra-class …
segmented. Because of their fuzzy borders, irregular boundaries, inter-and intra-class …
A novel approach for brain tumour detection using deep learning based technique
Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
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 …
Multi-class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)
M Heenaye-Mamode Khan, N Boodoo-Jahangeer… - Plos one, 2021 - journals.plos.org
The real cause of breast cancer is very challenging to determine and therefore early
detection of the disease is necessary for reducing the death rate due to risks of breast …
detection of the disease is necessary for reducing the death rate due to risks of breast …
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 …