Classification of Transgenic Mice by Retinal Imaging Using SVMS

Author:

Sayeed Farrukh1ORCID,Rafeeq Ahmed K.2,Vinmathi M. S.3,Priyadarsini A. Indira4,Gundupalli Charles Babu5,Tripathi Vikas6ORCID,Shishah Wesam7,Sundramurthy Venkatesa Prabhu8ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, ACE College of Engineering, Trivandrum 695027, Kerala, India

2. Department of Electronics and Communication Engineering, School of Engineering, Presidency University, Bangalore 560064, India

3. Department of Computer Science & Engineering, Panimalar Engineering College, Chennai 600123, Tamil Nadu, India

4. Department of Botany, SKR Govt. Degree College, Nagari, Andhra Pradesh 517590, India

5. Department of Computer Science & Engineering, Gokaraju Rangaraju Institute of Engineering and Technology (Autonomous), Kukatpally, Telangana 500090, India

6. Department of Computer Science & Engineering, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002, India

7. College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

8. Department of Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

Abstract

Alzheimer’s disease is the neuro disorder which characterized by means of Amyloid– β (A β ) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer’s disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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