Feature Extraction and Classification of Dementia with Neural Network

被引：0

作者：

Akhila, J. A. ^{[1
]}

Markose, Christin ^{[1
]}

Aneesh, R. P. ^{[2
]}

机构：

[1] Rajadhani Coll Engn & Technol, Dept ECE, Trivandrum, Kerala, India

[2] Reg Ctr IHRD, Trivandrum, Kerala, India

来源：

2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT) | 2017年

关键词：

Dementia; Neural Network; Magnetic Resonance Image (MRI); Segmentation-based Fractal Texture Analysis (SFTA);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Dementia, a neurodegenerative disorder related to aging, causes reduction in the cognitive abilities of man due to the degeneration of brain structures. Early detection of dementia with higher accuracy is essential for treatment. A technique for the accurate detection of Dementia from the Magnetic Resonance Images (MRI) of brain is proposed in this paper. Segmentation-based Fractal Texture Analysis (SFTA) technique is used for the extraction of features. Fractal dimensions and texture features are extracted from the binary images obtained after breaking down the image by the Two Threshold Binary Decomposition algorithm. Dementia classification is accomplished with Neural Network This algorithm is successfully tested using 3D brain MRI images obtained from the OASIS dataset with a classification accuracy of 97.5%.

引用

页码：1446 / 1450

页数：5

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