Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Literature review: Efficient deep neural networks techniques for medical image analysis
MA Abdou - Neural Computing and Applications, 2022 - Springer
Significant evolution in deep learning took place in 2010, when software developers started
using graphical processing units for general-purpose applications. From that date, the deep …
using graphical processing units for general-purpose applications. From that date, the deep …
A novel transfer learning based approach for pneumonia detection in chest X-ray images
Pneumonia is among the top diseases which cause most of the deaths all over the world.
Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the …
Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the …
Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
[HTML][HTML] High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images
In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Identifying pneumonia in chest X-rays: A deep learning approach
The rich collection of annotated datasets piloted the robustness of deep learning techniques
to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths …
to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths …
M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
In this study, we propose a three-dimensional Medical image classifier using Multi-plane
and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI …
and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI …
Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
The chest X-ray is one of the most commonly accessible radiological examinations for
screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging …
screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging …