Detecting Alzheimers Disease On Small Dataset
Shashank Nath
, Y. Pratyusha , D. Harshitha , N. Laxmi
Source image, Binary converted image, segmented image, KNN classify, median filter,
In recent years, the diagnosis of Alzheimer’s disease (AD) has become one of the most challenging problems in medical fields. This paper proposes a new segmentation method which is used region masking for selecting the useful properties of biological markers in the human brain for improving the accuracy of diagnosis for AD. In the proposed method, features of collected data sets, which can improve the accuracy of classification, are selected by using region masking. Furthermore, the different features are learning for the diagnosis of AD. The data set will be discussed in this paper contains normal and AD subjects. The empirical results show that the proposed method significantly improves the accuracy of the diagnosis of AD in comparison with previous methods.
"Detecting Alzheimers Disease On Small Dataset", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 5, page no.197 - 202, May-2020, Available :https://ijsdr.org/papers/IJSDR2005034.pdf
Volume 5
Issue 5,
May-2020
Pages : 197 - 202
Paper Reg. ID: IJSDR_191736
Published Paper Id: IJSDR2005034
Downloads: 000347183
Research Area: Engineering
Country: Hyderabad, Telangana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
