UGC Approved Journal no 63975(19)

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Published in:

Volume 5 Issue 8
August-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1808824


Registration ID:
185698

Page Number

477-487

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Title

AUTOMATIC CLASSIFICATION OF ASTROCYTE CELLS FOR TRAUMA USING SPATIAL AND TRANSFORM DOMAIN FEATURES

Abstract

Machine learning has enabled the way for designing modern computing systems that works in similar way as a human expert. It is rapidly becoming an essential part of data processing applications in which the size of data is enormous and manual processing is not feasible. Machine learning techniques find critical applications in areas where traditional computing methods fail to compute. For example, a traditional computing algorithm can be designed to find weather a given number is greater than, or smaller than, or equal to a specific number. However, traditional computing fails to figure out how similar a given image is to a specific image or how similar (or dissimilar) is the content written in two separate paragraphs. In such cognitive computing applications, machine learning techniques play a very significant role. A machine learning algorithm, and a classifier based on it can be implemented using neural networks, Bayesian networks, Fuzzy logic or Support Vector Machine (SVM). In this paper, a SVM based classifier is implemented to study the correlation properties for Astrocyte cells and the variations caused in human brain in case of Trauma. The proposed machine takes as input, the microscopic images of the astrocyte cells and classifies it into either normal or in trauma state. Image preprocessing is performed to extract the feature set which serves as inputs for classification. This paper extends the work of Najiya Nasrin et.al. by including, apart from the spatial feature set, the frequency domain characteristics that carries significant information of image texture. The simulation is performed on R statistical package and it turns out that proposed methods gives a more accurate classification as compared to the benchmark techniques.

Key Words

Brain Cells, Feature Extraction, Classification, Machine Learning, SVM

Cite This Article

"AUTOMATIC CLASSIFICATION OF ASTROCYTE CELLS FOR TRAUMA USING SPATIAL AND TRANSFORM DOMAIN FEATURES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.477-487, August-2018, Available :http://www.jetir.org/papers/JETIR1808824.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"AUTOMATIC CLASSIFICATION OF ASTROCYTE CELLS FOR TRAUMA USING SPATIAL AND TRANSFORM DOMAIN FEATURES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp477-487, August-2018, Available at : http://www.jetir.org/papers/JETIR1808824.pdf

Publication Details

Published Paper ID: JETIR1808824
Registration ID: 185698
Published In: Volume 5 | Issue 8 | Year August-2018
DOI (Digital Object Identifier):
Page No: 477-487
Country: Jaipur, Rajasthan, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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