In biomedical image analysis, information about the location and appearance of tumors and
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

The field of medical image processing plays a significant role in brain tumor classification.
The survival rate of patients can be increased by diagnosing the tumor at an early stage …

The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the
world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 …

Background and objective: Deep learning-based methods for fast target segmentation of
magnetic resonance imaging (MRI) have become increasingly popular in recent years …

Semi-supervised learning has made significant strides in the medical domain since it
alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Recently, methods based on convolutional neural networks have achieved good results in
the dense prediction of remote-sensing images, particularly when employing normalized …

Purpose Early-stage glaucoma diagnosis has been a challenging problem in
ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not …

Brain tumor segmentation remains a challenge in medical image segmentation tasks. With
the application of transformer in various computer vision tasks, transformer blocks show the …