Classification of Brain MR Images Using Corpus Callosum Shape Measurements

2015 ◽  
Vol 4 (2) ◽  
pp. 48-56 ◽  
Author(s):  
Gaurav Vivek Bhalerao ◽  
Niranjana Sampathila
Keyword(s):  
Neural Network ◽  
Corpus Callosum ◽  
Clustering Algorithm ◽  
Neurological Diseases ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mr Images ◽  
Male Patients ◽  
The Brain

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

2017 ◽  
pp. 1427-1436
Author(s):  
Gaurav Vivek Bhalerao ◽  
Niranjana Sampathila
Keyword(s):  
Neural Network ◽  
Corpus Callosum ◽  
Clustering Algorithm ◽  
Neurological Diseases ◽  
Back Propagation ◽  
Mr Images ◽  
Dementia Patients ◽  
Male Patients ◽  
The Brain

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

A back propagation neural network for the classification of visual field data

1993 ◽  
Vol 38 (9) ◽  
pp. 1263-1270 ◽  
Author(s):  
D Keating ◽  
E Mutlukan ◽  
A Evans ◽  
J McGarvie ◽  
B Damato
Keyword(s):  
Neural Network ◽  
Visual Field ◽  
Field Data ◽  
Back Propagation ◽  
Back Propagation Neural Network

scholarly journals Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network 

2014 ◽  
Vol 32 (No. 3) ◽  
pp. 280-287 ◽  
Author(s):  
I. Golpour ◽  
ParianJA ◽  
R.A. Chayjan
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Rice Cultivars ◽  
Image Processing Algorithm ◽  
White Rice ◽  
Colour Features

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

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