Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach

2017 ◽  
Vol 88 ◽  
pp. 152-164 ◽  
Author(s):  
Deepak Ranjan Nayak ◽  
Ratnakar Dash ◽  
Banshidhar Majhi ◽  
Vijendra Prasad
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Curvelet Transform ◽  
System A ◽  
Pathological Brain Detection ◽  
Fast Discrete Curvelet Transform

scholarly journals MRI Probabilistic Neural Network Screening System: a benign and malignant recognition case study

2021 ◽  
pp. 161-166
Author(s):  
Sami Hasan ◽  
Mays Yousif ◽  
Talib M. J. Al-Talib
Keyword(s):  
Neural Network ◽  
Brain Cancer ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Curvelet Transform ◽  
Gaussian Filter ◽  
System A ◽  
Network Screening ◽  
The Brain

This work is aimed to design a system which is able to diagnose two types of tumors in a human brain (benign and malignant), using curvelet transform and probabilistic neural network. Our proposed method follows an approach in which the stages are preprocessing using Gaussian filter, segmentation using fuzzy c-means and feature extraction using curvelet transform. These features are trained and tested the probabilistic neural network. Curvelet transform is to extract the feature of MRI images. The proposed screening technique has successfully detected the brain cancer from MRI images of an almost 100% recognition rate accuracy.

A Research of Virus Detection Combined Dynamic and Static Analysis Methods

2011 ◽  
Vol 187 ◽  
pp. 625-630
Author(s):  
Chun Yu Miao ◽  
Li Na Chen
Keyword(s):  
Neural Network ◽  
Static Analysis ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Virus Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis Methods ◽  
Individual Classifier ◽  
Theory Of Evidence

we present a virus detection system based on the D-S theory of evidence, in which the dynamic and static analysis methods are combined. The detection engine applies two types of classifier, support vector amchine and probabilistic neural network to detect the virus. For SVM classifier, we extract the feature vector by monitoring the samples. And the static feature of samples is used in the probabilistic neural network classifier. Finally, the D-S theory of evidence is used to combine the contribution of each individual classifier to give the final decision.experiments show the presented method is more efficiently of the virus detections.

scholarly journals Detection of Various Neoplasm’s in Medical Images Using Edge Detection and Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 59-62
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
The Body ◽  
Internal Cavity ◽  
Cell Detection ◽  
The People ◽  
Abnormal Cells ◽  
Occurrence Matrix

Brain tumor is one in all the extraordinary illness causes death among the people. Neoplasm is associate unconfined expansion of tissue in any neighborhood of the body. During the process have a tendency to tend to stand live taking man photos as input; resonance imaging that is guided into internal cavity of brain and offers the entire image of brain. In this paper brain tumor detection system is proposed. Here bunch methodology supported intensity was enforced. The Probabilistic Neural Network square measure used to identify the various levels of tumor like Malignant, Benign or traditional. PNN with Radial Basis are used for classification and segmentation of cells. In order to classify the normal or abnormal cells, proper decision need to be taken. This could be done in 2 levels: Gray-Level Co-occurrence Matrix and the classification are performed based on Neural Networks. The tumor cell detection is manually performed by the schematic methodology for X-radiation.

scholarly journals Evaluation of Performance of Decision Tree, Support Vector Machine and Probabilistic Neural Network Classifiers in a Mobile Based Diabetes Retinopathy Detection System

2019 ◽  
pp. 1-9
Author(s):  
O. D. Fenwa ◽  
O. O. Alo ◽  
I. O. Omotoso
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Decision Tree ◽  
Medical Condition ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Rejection Rate ◽  
Support Vector ◽  
Diabetes Retinopathy

Diabetic Retinopathy (DR) is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. Aim: The focus of this paper is to evaluate the performance of Decision Tree (DT), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) Classifiers in Diabetes Retinopathy Detection. Results: Corresponding results showed SVM has the best classification strength by achieving Recognition Accuracy (RA) of 98.50%, while PNN and DT achieved RA of 97.60% and 89.20% respectively. In terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), SVM has the least values of 7.21, 8.10 while DT and PNN showed 11.10, 9.30 and 13.21, 10.10 respectively. However, in this paper a Mobile based Diabetes Retinopathy Detection System was developed to make the system available for the masses for early detection of the disease.

A Pathological Brain Detection System Based on Radial Basis Function Neural Network

2016 ◽  
Vol 6 (5) ◽  
pp. 1218-1222 ◽  
Author(s):  
Zhihai Lu ◽  
Siyuan Lu ◽  
Ge Liu ◽  
Yudong Zhang ◽  
Jianfei Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Detection System ◽  
Pathological Brain Detection ◽  
Radial Basis

Offline Handwritten Character & Numeral Recognition A Kernel Based Approach

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Character Recognition ◽  
Probabilistic Neural Network ◽  
Research Work ◽  
Curvelet Transform ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Indic Script ◽  
Fast Discrete Curvelet Transform ◽  
Numeral Recognition

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.

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