Robust Blood Vessels Segmentation Based on Memory-Augmented Neural Network

2020 ◽  
pp. 425-433
Author(s):  
K. Arunabhaskar ◽  
R. Kiran Kumar
Keyword(s):  
Neural Network ◽  
Blood Vessels

scholarly journals Retinal blood vessels segmentation using classical edge detection filters and the neural network

2021 ◽  
Vol 23 ◽  
pp. 100521
Author(s):  
Beaudelaire Saha Tchinda ◽  
Daniel Tchiotsop ◽  
Michel Noubom ◽  
Valerie Louis-Dorr ◽  
Didier Wolf
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Blood Vessels ◽  
The Neural Network ◽  
Retinal Blood Vessels

Classification and grading of diabetic retinopathy images using mixture of ensemble classifiers

2021 ◽  
pp. 1-13
Author(s):  
R. Bhuvaneswari ◽  
S. Ganesh Vaidyanathan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Support Vector ◽  
Network Architectures ◽  
Ensemble Classifiers ◽  
Feature Maps ◽  
Class Labels ◽  
Hierarchical Features

Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided.

Retinal blood vessels detection for diabetic retinopathy with Ridgelet transform and convolution neural network

2020 ◽  
Vol 18 (06) ◽  
pp. 2050048
Author(s):  
Nirmal Yadav
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Transform Method ◽  
Ridgelet Transform ◽  
Study Results

Applying machine learning in life sciences, especially diagnostics, has become a key area of focus for researchers. Combining machine learning with traditional algorithms provides a unique opportunity of providing better solutions for the patients. In this paper, we present study results of applying the Ridgelet Transform method on retina images to enhance the blood vessels, then using machine learning algorithms to identify cases of Diabetic Retinopathy (DR). The Ridgelet transform provides better results for line singularity of image function and, thus, helps to reduce artefacts along the edges of the image. The Ridgelet Transform method, when compared with earlier known methods of image enhancement, such as Wavelet Transform and Contourlet Transform, provided satisfactory results. The transformed image using the Ridgelet Transform method with pre-processing quantifies the amount of information in the dataset. It efficiently enhances the generation of features vectors in the convolution neural network (CNN). In this study, a sample of fundus photographs was processed, which was obtained from a publicly available dataset. In pre-processing, first, CLAHE was applied, followed by filtering and application of Ridgelet transform on the patches to improve the quality of the image. Then, this processed image was used for statistical feature detection and classified by deep learning method to detect DR images from the dataset. The successful classification ratio was 98.61%. This result concludes that the transformed image of fundus using the Ridgelet Transform enables better detection by leveraging a transform-based algorithm and the deep learning.

scholarly journals A neural network approach to segment brain blood vessels in digital subtraction angiography

2020 ◽  
Vol 185 ◽  
pp. 105159 ◽  
Author(s):  
Min Zhang ◽  
Chen Zhang ◽  
Xian Wu ◽  
Xinhua Cao ◽  
Geoffrey S. Young ◽  
...  
Keyword(s):  
Neural Network ◽  
Digital Subtraction Angiography ◽  
Blood Vessels ◽  
Digital Subtraction ◽  
Network Approach ◽  
Neural Network Approach

An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy

2017 ◽  
Vol 228 ◽  
pp. 143-153 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Nicola Pietroleonardo ◽  
Vito Triggiani ◽  
Antonio Brunetti ◽  
Anna Maria Di Palma ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Vessels ◽  
Kidney Biopsy ◽  
Histological Images ◽  
Haralick Features

scholarly journals Penerapan Algoritma Convolutional Neural Network dalam Klasifikasi Telur Ayam Fertil dan Infertil Berdasarkan Hasil Candling

2021 ◽  
Vol 5 (4) ◽  
pp. 563
Author(s):  
Muhammad Rizky Firdaus
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Prediction Errors ◽  
Network Method ◽  
Hatching Process ◽  
Egg Shell ◽  
Chicken Eggs ◽  
Selection Of

Fertile chicken eggs are eggs that can hatch because these eggs have a development in the form of dots of blood and blood vessels or can be called an embryo, while infertile chicken eggs are a type of egg that cannot be hatched because there is no embryo development in the hatching process. Inspection of infertile chicken eggs must be carried out especially for breeders who will carry out the selection and transfer of fertile chicken eggs and infertile chicken eggs. However, currently, the selection of fertile and infertile chicken eggs is still using a less effective way, namely only by looking at the egg shell or called candling, this process is certainly less accurate to classify which eggs are fertile and infertile eggs because not all breeders are able to see the results of the eggs properly. candling so that the possibility of prediction errors. Therefore, in this study, a classification of fertile chicken eggs and infertile chicken eggs will be carried out based on candling results using the Convolutional Neural Network method. From the results of the classification carried out, the percentage of accuracy obtained for the classification of fertile and infertile chicken eggs is 98% and an error of 5%.

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