Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
R Adam, K Dell'Aquila, L Hodges, T Maldjian… - Breast Cancer …, 2023 - Springer
Deep learning analysis of radiological images has the potential to improve diagnostic
accuracy of breast cancer, ultimately leading to better patient outcomes. This paper …
accuracy of breast cancer, ultimately leading to better patient outcomes. This paper …
A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances
Background Nutrition plays a vital role in maintaining human health. Traditional methods
used for assessing food composition and nutritional content often require destructive sample …
used for assessing food composition and nutritional content often require destructive sample …
Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence
Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all
new lung cancer diagnoses and a 30–55% of recurrence rate after surgery. Thus, an …
new lung cancer diagnoses and a 30–55% of recurrence rate after surgery. Thus, an …
A comparative analysis of deep learning convolutional neural network architectures for fault diagnosis of broken rotor bars in induction motors
Induction machines (IMs) play a critical role in various industrial processes but are
susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic …
susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic …
Logarithmic Learning Differential Convolutional Neural Network
Abstract Convolutional Neural Networks (CNNs) have revolutionized image classification
through their innovative design and training methodologies in computer vision. Differential …
through their innovative design and training methodologies in computer vision. Differential …
An instance-based deep transfer learning method for quality identification of Long**g tea from multiple geographical origins
C Zhang, J Wang, T Yan, X Lu, G Lu, X Tang… - Complex & Intelligent …, 2023 - Springer
For practitioners, it is very crucial to realize accurate and automatic vision-based quality
identification of Long**g tea. Due to the high similarity between classes, the classification …
identification of Long**g tea. Due to the high similarity between classes, the classification …
[HTML][HTML] Next-Gen medical imaging: U-Net evolution and the rise of transformers
The advancement of medical imaging has profoundly impacted our understanding of the
human body and various diseases. It has led to the continuous refinement of related …
human body and various diseases. It has led to the continuous refinement of related …
DDC3N: Doppler-Driven Convolutional 3D Network for Human Action Recognition
In deep learning (DL)–based human action recognition (HAR), considerable strides have
been undertaken. Nevertheless, the precise classification of sports athletes' actions still …
been undertaken. Nevertheless, the precise classification of sports athletes' actions still …
Non-contact measurement of pregnant sows' backfat thickness based on a hybrid CNN-ViT model
Backfat thickness (BF) is closely related to the service life and reproductive performance of
sows. The dynamic monitoring of sows' BF is a critical part of the production process in large …
sows. The dynamic monitoring of sows' BF is a critical part of the production process in large …