… Neural Network or Convolutional Neural Network from NN family. The impression …
Image Segmentation methodology is a part of almost all computer vision systems as a pre-processing …

… convolutional neural networks? The computer vision has various tasks among them image
segmentation … Various researchers are addressing this task in different way using traditional …

… convolution kernels that are specifically trained for the classification problem at hand. While
classical machine learning methods applied to image segmentation would use … , the system …

… image segmentation task using deep learning is presented by providing the code. In general,
most of the work using DL techniques use an … these techniques to various medical image …

… used framework – convolutional neural networks (CNN) is … the deep networks developed
for image segmentation. Under … for image segmentation compared to the ordinary system, …

… Other methods known as Deep Learning deal with repre… [26], [27] and biological image
segmentation [28] challenges. … Finally, we subtract each metric for the two systems and calculate …

… BAT algorithm for automatic segmentation method. The primary aim is to present optimization
based MRIs image segmentation. Small kernels allow the design in a deep architecture. It …

… line, real-time system which can capture … method to distinguish belt ores of different size
distributions [2]. Lin and Miller developed an image-based system which used image processing …

… , automatic image segmentation can give us insights into how the human visual system is …
information systems; it can give us deep insight into the conditions when visual information …

… We propose a Convolutional Neural Network (CNN) approach which is amongst the top
performing methods while also … In terms of image segmentation, Ciresan et al. [4] exploblack …