Real-world data: a brief review of the methods, applications, challenges and opportunities
F Liu, D Panagiotakos - BMC Medical Research Methodology, 2022 - Springer
Background The increased adoption of the internet, social media, wearable devices, e-
health services, and other technology-driven services in medicine and healthcare has led to …
health services, and other technology-driven services in medicine and healthcare has led to …
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 …
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …
death cases and affected all sectors of human life. With gradual progression of time, COVID …
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 deep learning system for differential diagnosis of skin diseases
Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most
cases are seen instead by general practitioners with lower diagnostic accuracy. We present …
cases are seen instead by general practitioners with lower diagnostic accuracy. We present …
Activation functions: Comparison of trends in practice and research for deep learning
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …
real world complex problems with may more deep learning (DL) architectures, being …
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning
Breast cancer is among the leading cause of mortality among women in developing as well
as under-developing countries. The detection and classification of breast cancer in the early …
as under-developing countries. The detection and classification of breast cancer in the early …
Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has
infected more than 1,436,000 people in more than 200 countries and territories as of April 9 …
infected more than 1,436,000 people in more than 200 countries and territories as of April 9 …
Object detection using deep learning, CNNs and vision transformers: A review
Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …
most fundamental and challenging aspects. Significant advances in object detection have …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …