Image Processing Convolutional neural networks have achieved groundbreaking success with large data sets in critical computer vision tasks (eg, object recognition, image segmentation) as well as health-related applications (eg, recognition of skin cancer)42 and estimating poverty.43 Owing to the lack ...
After receiving the necessary information concerning the disease, the essential treatment procedures are deliberated to limit/cure the disease. This work implements a hybrid approach based on the heuristic algorithm supported multilevel thresholding and image segmentation. This approach is experimentally ...
This work implements a hybrid approach based on the heuristic algorithm supported multi-level thresholding and image segmentation. This approach is experimentally inspected using the well known RIM-ONE benchmark retinal database. This work implements the pre-processing based on Jaya algorithm guided ...
Kapur's entropyChan-Vese segmentationImage analysisImage examination is widely considered in medical field for computer-supported disease evaluation. Brain cancer is the deadliest cancer and requires image/signal processing approaches to record and......
Tsallis entropyDeformable snakePerformance metricsImage segmentation plays a vital role in various medical applications for automated disease examination. In this paper, heuristic algorithm assisted approach is proposed to extract the tumor from a two dimensional (2D) magnetic resonance image. The proposed...
Finally, the three codes were developed into the following practical hints for effective use of SNS in marketing: the importance of target segmentation, compliance with manners in SNS communities, and added value for SNS users.Fielding Graduate University.Tonaka, Yoko....
Using data from the Sentinel-2 satellite, Mirpulatov et al. [10] suggested a way to fix the problem of incorrect and low-resolution markup in land cover and land use segmentation tasks. They called it a “pipeline for pseudo-labeling”. Praticò et al. [11] employed random forest (RF...