D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI …
Background and Objective: In 2019, a corona virus disease (COVID-19) was detected in
China that affected millions of people around the world. On 11 March 2020, the WHO …
China that affected millions of people around the world. On 11 March 2020, the WHO …
Superpixel-based multiscale CNN approach toward multiclass object segmentation from UAV-captured aerial images
Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming
remote sensing applications. However, for artificial-intelligence-guided tasks, such as land …
remote sensing applications. However, for artificial-intelligence-guided tasks, such as land …
Explainable human‐in‐the‐loop healthcare image information quality assessment and selection
Smart healthcare applications cannot be separated from healthcare data analysis and the
interactive interpretability between data and model. A human‐in‐the‐loop active learning …
interactive interpretability between data and model. A human‐in‐the‐loop active learning …
3D kronecker convolutional feature pyramid for brain tumor semantic segmentation in MR imaging
Brain tumor significantly impacts the quality of life and changes everything for a patient and
their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging …
their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging …
An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
Brain tumors in Magnetic resonance image segmentation is challenging research. With the
advent of a new era and research into machine learning, tumor detection and segmentation …
advent of a new era and research into machine learning, tumor detection and segmentation …
Brain Tumor Segmentation Based on α‐Expansion Graph Cut
In recent years, there has been an increased interest in using image processing, computer
vision, and machine learning in biological and medical imaging research. One area of this …
vision, and machine learning in biological and medical imaging research. One area of this …
Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified U‐Net model
Early diagnosis of multiple sclerosis (MS) through the delineation of lesions in the brain
magnetic resonance imaging is important in preventing the deteriorating condition of MS …
magnetic resonance imaging is important in preventing the deteriorating condition of MS …
Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks
This research comprises experiments with a deep learning framework for fully automating
the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for …
the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for …
Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method
SK Roy, B Rudra - International Journal of Imaging Systems …, 2024 - Wiley Online Library
Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on
how patients turn out. Conventional detection techniques can be laborious and demand a lot …
how patients turn out. Conventional detection techniques can be laborious and demand a lot …
MAEU‐NET: A novel supervised architecture for brain tumor segmentation
A brain tumor is an abnormal growth of cells that damages the neural system and may lead
to severe conditions. These cells are irregular in shape and size, and as a result, the manual …
to severe conditions. These cells are irregular in shape and size, and as a result, the manual …