Classification of Transgenic Mice by Retinal Imaging Using SVMS

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.

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules

“…Logistics performs better in solving the data linearly and is more robust than linear regression when outliers are present in the dataset (11)(12)(13). SVM is among the supervised learning algorithms for classification, which applies the radial basis kernel function to establish maximal margin hyperplane in higher data space, enabling the differentiation between different datasets (14). In general, SVM exhibits computational complexity and is capable of solving both linear and non-linear classification problems (15,16).…”