Detection of Attention Deficit Hyperactivity Disorder Using Local and Global Features

Attention deficit hyperactivity disorder (ADHD) is a psychiatric condition that affects millions of children and many times last into adulthood. There is no single test that can show whether a person has ADHD. The symptoms vary from person to person. Therefore, it is hard to diagnose ADHD contrary to many physical illnesses. Our aim is to create methods to minimize human effort and increase accuracy of diagnosis of ADHD. We collected structural Magnetic Resonance Images (MRI) from 26 subjects: 11 controls and 15 children diagnosed with ADHD. The data was provided from NPIstanbul NeuroPsychiatric Hospital. We developed automatic, effective, rapid, and accurate framework for diagnosing ADHD. The models were built on the k-nearest neighbors algorithm (KNN) and naive Bayes using Matlab machine learning toolbox. Shape and texture feature extraction technique was used. Area, Perimeter, Eccentricity, EquivDiameter, Major Axis Length, Minor Axis Length, Orientation are characteristics used for shape feature extraction technique. Textural features of a magnetic resonance imaging were represented with first (mean, variance, skewness, kurtosis) and second order statistical (contrast, correlation, homogeneity, energy) based feature extraction techniques. Gray and white regions were extracted using k-means algorithms. Local features were extracted from these regions by shape and texture methods. Global features were extracted with second order statistics which is called gray level co-occurrence method. The most important attribute was determined by using principal component analysis. The experiments were conducted on a full training dataset including 26 instance and 5 fold cross validation was adopted for randomly sampling training and test sets. ADHD is successfully classified with 100 % accuracy by using the proposed method. The outcome of our study will reduce the number medical errors by informing physicians in their efforts of diagnosing ADHD.