The Impact Of Neural Network Techniques In The Optimization Of The Image Processing

Abstract

Image processing is widely utilized recently in many applications of civilian lives and for various purposes. Scholars proposed and suggested various techniques of image processing. One of the main processing techniques is the neural network, which is the state of art in field nowadays. This paper aims to reveal the neural networks techniques in image processing types. Moreover, this paper focuses on the impact of the neural networks in optimizing medical image processing due to importance of medical imaging and the observed trends of utilizing digital medical imaging in the health sectors. In this context, the early diagnosis and detection of the eye has important role in the avoidance of visual impairment, due to the fact that around 45 billion people have visual impairments all over the world according to world health organization. For this reason, the current paper introduce new method based on image processing for vascular segmentation based on morphological active counter, the model input id the fundus images processed using filters techniques, then segmentation carried out based on morphological operations, fuzzy c-means and watershed transform. The output of such segmentation methods were given to the CNN. The optimized feature values are then extracted. The threshold value is set to compare this optimized feature values. From this, the best segmentation methods will be obtained.