U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
A survey on multi-objective hyperparameter optimization algorithms for machine learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …
performance of Machine Learning (ML) algorithms. Several methods have been developed …
Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation
Medical image segmentation is the most complex and important task in the field of medical
image processing and analysis, as it is linked to disease diagnosis accuracy. However, due …
image processing and analysis, as it is linked to disease diagnosis accuracy. However, due …
Deep learning models for COVID-19 infected area segmentation in CT images
Recent studies indicated that detecting radiographic patterns on CT chest scans can yield
high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the …
high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the …
A survey on evolutionary construction of deep neural networks
Automated construction of deep neural networks (DNNs) has become a research hot spot
nowadays because DNN's performance is heavily influenced by its architecture and …
nowadays because DNN's performance is heavily influenced by its architecture and …
Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis
OAMF Alnaggar, BN Jagadale, MAN Saif… - Artificial Intelligence …, 2024 - Springer
In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray,
PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection …
PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection …
Automatic cardiac cine MRI segmentation and heart disease classification
Cardiac cine magnetic resonance imaging (MRI) continues to be recognized as an
established modality for non-invasive assessment of the function and structure of the …
established modality for non-invasive assessment of the function and structure of the …
Neural architecture search survey: A computer vision perspective
JS Kang, JK Kang, JJ Kim, KW Jeon, HJ Chung… - Sensors, 2023 - mdpi.com
In recent years, deep learning (DL) has been widely studied using various methods across
the globe, especially with respect to training methods and network structures, proving highly …
the globe, especially with respect to training methods and network structures, proving highly …
Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification
Automatic classification of fruit freshness plays an important role in the agriculture industry.
In this work, we propose an ensemble model that combines the bottleneck features of two …
In this work, we propose an ensemble model that combines the bottleneck features of two …