The Impact of Neural Network Techniques in the Optimization of the Image Processing

Abstract

Nowadays, image processing is widely utilized in many applications and for various purposes. Scholars proposed and suggested various techniques of image processing. The neural network is one of the main processing techniques, which is a state-of-art method. This paper aims to investigate neural network techniques in the field of image processing. Moreover, medical imaging, as well as increasing trends of utilizing digital medical imaging, has gained huge attention in the health sectors. In this regard, this paper focuses on the effect of neural networks in optimizing medical image processing. In this context, the early diagnosis and detection of the eye have an important role in the avoidance of visual impairment, because of the fact that around 45 million people have visual impairments all over the world, according to the World Health Organization. For this reason, the current paper introduces a new method based on image processing for vascular segmentation based on a morphological active contour. Then, segmentation carried out based on morphological operations, fuzzy c-means, and watershed transform. The output of such segmentation methods was given to conventional neural network. The optimized feature values are then extracted. The threshold value is set to compare these optimized feature values. From this, the best segmentation methods will be obtained.

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

“…Such pixels combine into sets of segments, and neighboring image segments will differ in the same characteristics. Due to new emerging opportunities, artifi-*tatarkanov@ikti.ru cial NNs have become in high demand in recent years to analyze complex structured images resulting from medical imaging [15][16][17][18]42]. A prime example of a successful application of NNs is the RatLesNetv2 model [19].…”