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 …

Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …

The prediction of cancer characteristics, treatment planning and patient outcome from
medical images generally requires tumor delineation. In Head and Neck cancer (H&N), the …

Oil spills are considered to be one of the biggest obstacles to marine and coastal
environments. Effective surveillance, ship detection, and accurate oil spill detection are …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Marine pollution poses a humongous threat to oceanic life especially large scale oil spills.
The paper addresses this concern by offering a comparative study of novel deep learning …

The human retina contains a network of micro-vessels that helps predict chronic diabetic
retinopathy. The disease is common in patients with diabetes mellitus. It weakens the inner …

Brain tumours are a frequent cause of mortality in both adults and children, ranking tenth
among major grounds of fatality. There are a great number of distinct types of tumours, and …

U-net is an image segmentation technique developed primarily for medical image analysis
that can precisely segment images using a scarce amount of training data. These traits …

Optical signal communication and computation are very popular these days. Therefore,
optical-based different logic gates, combinational circuits, devices, etc. are developed by …