Hypertensive Retinopathy Classification Using Improved Clustering Algorithm and the Improved Convolution Neural Network

Hypertensive retinopathy is a disorder that causes hypertension which includes abnormalities in the retina that triggers vision problems. An effective automatic diagnosis and grading of the hypertensive retinopathy would be very useful in the health system. This chapter presents an improved activation function on the CNN by recognizing the lesions present in the retina and afterward surveying the influenced retina as indicated by the hypertensive retinopathy various sorts. The current approach identifies the symptoms associated of retinopathy for hypertension. This chapter presents an up-to-date review on hypertensive retinopathy detection systems that implement a variety of image processing techniques, including fuzzy image processing, along various improved activation function techniques used for feature extraction and classification. The chapter also highlights the available public databases, containing eye fundus images, which can be currently used in the hypertensive retinopathy research.

Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention.

The inertial relaxed algorithm with Armijo-type line search for solving multiple-sets split feasibility problem

The multiple-sets split feasibility problem is the generalization of split feasibility problem, which has been widely used in fuzzy image reconstruction and sparse signal processing systems. In this paper, we present an inertial relaxed algorithm to solve the multiple-sets split feasibility problem by using an alternating inertial step. The advantage of this algorithm is that the choice of stepsize is determined by Armijo-type line search, which avoids calculating the norms of operators. The weak convergence of the sequence obtained by our algorithm is proved under mild conditions. In addition, the numerical experiments are given to verify the convergence and validity of the algorithm.

Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering

The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.