Adhd Classification from FMRI Data Using Fine Tunining in SVM

Abstract

ML (Machine learning) is a subset of AI and also improved learning technique has different performance in result over the conventional ML determining the complexity in structures of dimensional data. ADHD is one of the most important neurological disorders and it is represented by different symptoms and we can extract useful the information from FMRI time series. In this paper the ADHD identification and classification is obtained by machine learning techniques. This paper explores an artificial intelligence in unsupervised learning is appropriate to learn features from raw data. The proposed system presented with two stage approaches for ADHD diagnosis which associated SoftMax Regression and SVM fine tuning approach. In the implementation part used FMRI brain images are data sets. The two stage approach shows the high accuracy in performance by using the learning techniques.