An Evaluation of Structural Brain Tissue Abnormalities to Diagnose Attention Deficit Hyperactivity Disorder
E. Radhamani1, K. Krishnaveni2

1E. Radhamani, Research Scholar, Sri S.R.amasamy Naidu Memorial College, Sattur, Tamil Nadu, India.
2K. Krishnaveni, Associate Professor, Sri S.R.amasamy Naidu Memorial College, Sattur, Tamil Nadu, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2366-2372 | Volume-9 Issue-1, October 2019 | Retrieval Number: F91801088619/2019©BEIESP | DOI: 10.35940/ijeat.F9180.109119
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Abstract: Attention-deficit/hyperactivity disorder (ADHD) is highly associated with brain abnormality. In this paper, an evaluation of structural brain tissue abnormalities to diagnose ADHD is proposed. A Brain Image Data Set containing 40, T2 axial Human ADHD Brain MR Images is created, normalized, preprocessed by Skull stripping method and Caudate Nucleus(CN), Grey Matter(GM) and White Matter(WM)data sets are formed by segregating the structural brain parts CN, GM and WM using Fuzzy C-Means algorithm. The texture, intensity and shape based features of each data set is extracted, and the images with abnormal (reduced) volume and shape are classified with the help of Pearson’s chi-squared (χ2) test by statistical measures and machine learning classifiers. The results show that out of 40, the brain tissues of 25 ADHD brain images are found with reduced volume and size of all CN, GM and WM and remaining images are found with at least any one of these abnormality compared to control group. The findings confirm that ADHD is a brain disorder which mainly affects the growth of the brain tissues and recommends the physicians to initiate the diagnosis process of ADHD with MRI brain scans instead of high risk medication and counseling among the children.
Keywords: Attention-deficit/hyperactivity disorder, MR Images Caudate Nucleus, Grey Matter, White Matter, Skull stripping, Fuzzy C-Means, Pearson’s chi-squared test, machine learning classifiers.