Filters








4,174 Hits in 10.9 sec

Comparison of Detection and Classification of Hard Exudates Using Artificial Neural System vs. SVM Radial Basis Function in Diabetic Retinopathy

V. Sudha, T. R. Ganesh Babu, N. Vikram, R. Raja
2021 MCB Molecular and Cellular Biomechanics  
The evaluation results showed that proposed artificial neural network based on fuzzy approach attained significant results compared to other classifiers.  ...  The motivation behind this research is to reduce the number of false positives by accurate detection which is possible using proposed fuzzy system based on ANN.  ...  A support vector machine improves the detection accuracy by using a regression and classification technique utilising machine learning technique.  ... 
doi:10.32604/mcb.2021.016056 fatcat:jbpdzo2nefhfpkrc2tijmxisqq

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
645 3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection 646 A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis  ...  Detection of Inner Ears in Head CTs Using a Deep Volume-to-Volume Regression Network with False Positive Suppression and a Shape-Based Constraint 639 How to Cure Cancer with Unpaired Image Translation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Comparison of Detection and Classification of Hard Exudates using Artificial Neural System vs SVM Radial Basis Function in Diabetic Retinopathy

V. Sudha, T. R. Ganesh Babu, R. Raja
2020 International Journal of Darshan Institute on Engineering Research & Emerging Technology  
The evaluation results showed that proposed artificial neural network based on fuzzy approach attained significant results compared to other classifiers.  ...  The motivation behind this research is to reduce the number of false positives by accurate detection which is possible using proposed fuzzy system based on ANN.  ...  A support vector machine improves the detection accuracy by using a regression and classification technique utilising machine learning technique.  ... 
doi:10.32692/ijdi-eret/9.1.2020.2004 fatcat:ooxljocjrvchhefuf33ljezg7i

A survey on hemorrhage detection in diabetic retinopathy retinal images

Parisut Jitpakdee, Pakinee Aimmanee, Bunyarit Uyyanonvara
2012 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology  
Early automated hemorrhage detection can help reduce the incidence of blindness.  ...  Diabetic Retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina.  ...  [10] detected red lesions using multilayer perceptron neural network. The algorithm was tested on 50 images with a set of 29 features that describe the shape and color of image regions.  ... 
doi:10.1109/ecticon.2012.6254356 fatcat:5fwcpm24sfhgbg42ekoyewzz4u

Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report [article]

Sharath M Shankaranarayana
2020 arXiv   pre-print
We leverage the recent state of the art deep networks for building a single fundus image based AMD classification pipeline.  ...  We propose the use of generative adversarial networks (GANs) for the tasks of segmentation and detection. We also propose a novel method of fovea detection using GANs.  ...  Acknowledgement We thank the organizers of the Automatic Detection challenge on Age-related Macular degeneration (ADAM) (https://amd.grand-challenge.org/) for hosting the challenge and kindly providing  ... 
arXiv:2009.01548v1 fatcat:dwknxv6xtjbu3mj2w5svlyqheq

UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

Srinivasan A, Sudha S
2017 Asian Journal of Pharmaceutical and Clinical Research  
Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions.  ...  Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists.  ...  classification by hybrid classifier (m-Mediods based classifier with a Gaussian mixture model [GMM]).  ... 
doi:10.22159/ajpcr.2017.v10i4.17023 fatcat:taxpycv3snbfdepmb4pylfq7iy

Computational vision systems for the detection of malignant melanoma

Ilias Maglogiannis, Dimitrios Kosmopoulos
2006 Oncology Reports  
features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines.  ...  We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them.  ...  Both comparisons were made with linear discriminant analysis, by fitting a neural network model and utilizing the SVM algorithm.  ... 
doi:10.3892/or.15.4.1027 fatcat:5nrgevq2mfa6rfakrujs4t42hm

A REVIEW ON IMPLEMENTATION OF ALGORITHMS FOR DETECTION OF DIABETIC RETINOPATHY

A.P.Shingade .
2014 International Journal of Research in Engineering and Technology  
A completely automated screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time.  ...  Diabetes is a group of metabolic disease in which a person has high blood sugar. Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body.  ...  If the neural network classifier is used then the output of a network is in the range of 0 to 1. A neural network classifier with multilayer perceptron is used [23] .  ... 
doi:10.15623/ijret.2014.0303016 fatcat:7locc7o4hzgdleswigx4twdykq

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

Yan Ling Yong, Li Kuo Tan, Robert A. McLaughlin, Kok Han Chee, Yih Miin Liew
2017 Journal of Biomedical Optics  
We propose a linearregression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space.  ...  The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.  ...  convolutional neural network for fully automated coronary lumen segmentation in intravascular. . .  ... 
doi:10.1117/1.jbo.22.12.126005 pmid:29274144 fatcat:5i3qrzxw5remdfinsdjoybck54

Comparative Study of Classification System using K-NN, SVM and Ada-boost for Multiple Sclerosis and Tumor Lesions using Brain MRI

Rupali Kamathe, Kalyani Joshi
2016 International Journal of Multimedia and Image Processing  
Study of the medical image by the radiologist is a time consuming process and also the accuracy depends upon their experience.  ...  This paper presents an automated process of classification of Multiple sclerosis and Tumor lesions from brain MRI in which 3 models for classification of lesions is considered as: i.  ...  [12] , presented hybrid method based on convolution neural network (CNN) for features extraction and a multilayer neural network for classification into two classes normal and MS.  ... 
doi:10.20533/ijmip.2042.4647.2016.0040 fatcat:ozvouwvl7jbffdpmtgjqoufk5e

Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks [article]

Florian Dubost, Hieab Adams, Pinar Yilmaz, Gerda Bortsova, Gijs van Tulder, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
2020 arXiv   pre-print
We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions.  ...  We study the behavior of the proposed method in MNIST-based detection datasets, and evaluate it for the challenging detection of enlarged perivascular spaces - a type of brain lesion - in a dataset of  ...  This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) Project 104003005, with additional support of Netherlands Organisation for Scientific Research (NWO)  ... 
arXiv:1906.01891v4 fatcat:xzjplmpj6zamnpuyfk6q2porda

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
In many applications, machine learning based systems have shown comparable performance to human decision-making.  ...  By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.  ...  Acknowledgment Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

DeepCADx

Zhiwei Wang, Chaoyue Liu, Xiang Bai, Xin Yang
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
In this paper, we present DeepCADx, a computer-aided prostate detection and diagnosis (CADx) system powered by a novel deep convolutional neural networks (CNNs).  ...  Gleason score) of each localized lesion using multimodal CNN features and a 5-class SVM classifier.  ...  Images in Fig. 2(a) are the original ADC-T2w slice pair, and images in Fig. 2(b) are the prostate images extracted by the pre-processing, and the image in Fig. 2(c) is the ADC slice overlaid with  ... 
doi:10.1145/3123266.3127914 dblp:conf/mm/WangLBY17 fatcat:56rbwtegjrfsldcvf4absh3sti

Intelligent Screening Systems for Cervical Cancer

Yessi Jusman, Siew Cheok Ng, Noor Azuan Abu Osman
2014 The Scientific World Journal  
The computer system based on cytology data and electromagnetic spectra data achieved better accuracy than other data.  ...  The cytology combined with neural network gives the accuracy of up to 99% of accuracy to classify 400 data to be 2 classes, followed by neural network using the electromagnetic spectra features at 97.4%  ...  system is neural network.  ... 
doi:10.1155/2014/810368 pmid:24955419 pmcid:PMC4037632 fatcat:yzsg5kze6fadjpyfgiosecp52m

Histogram-Based Texture Characterization and Classification of Brain Tissues in Non-Contrast CT Images of Stroke Patients [chapter]

Kenneth K. Agwu, Christopher C. Ohagwu
2016 Pattern Recognition - Analysis and Applications  
The artificial neural network (ANN) and k-nearest neighbour (k-NN) algorithms were used to classify the ROIs into normal tissue, ischaemic and haemorrhagic lesions using the radiologists' categorization  ...  This chapter describes histogram-based texture characterization and classification of brain tissue in CT images of stroke patients using a case study. It explored texture analysis in medical imaging.  ...  At this stage of its development, detection of lesions and their interpretation is becoming an automated computer-aided process.  ... 
doi:10.5772/65349 fatcat:mn7fjvdxw5cildz2iajmkwusne
« Previous Showing results 1 — 15 out of 4,174 results