As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important …
MI Razzak, S Naz, A Zaib - … in BioApps: Automation of decision making, 2018 - Springer
The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health …
In the recent decade, deep learning has taken lead over available analysis techniques. Today's deep learning is used in diversified sectors like health care, traffic management …
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical images. They have been used extensively for medical image segmentation as the first and …
R Wang, T Lei, R Cui, B Zhang, H Meng… - IET image …, 2022 - Wiley Online Library
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A …
Medical image segmentation is a vital task in medical imaging, aiming to extract meaningful and precise information from images. While traditional methods have been extensively used …
P Malhotra, S Gupta, D Koundal… - Journal of …, 2022 - Wiley Online Library
Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many …
J Moorthy, UD Gandhi - Big Data and Cognitive Computing, 2022 - mdpi.com
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the …
The performance of a Convolutional Neural Network (CNN) highly depends on its architecture and corresponding parameters. Manually designing a CNN is a time-consuming …