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Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorder in childhood and adolescence. ADHD diagnosis currently includes psychological tests and depends on ratings of behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based on non-invasive imaging can improve the understanding and diagnosis of ADHD. The purpose of this study is classifying brain images by using Artificial Intelligence methods such as clinical decision support system for the diagnosis of ADHD. For this purpose and according to a medical imaging classification system, firstly, image pre-processing is done. Then, a deep multi-modal 3D CNN is trained on GM from structural and fALFF from functional MRI using ADHD-200 training dataset. Finally, with the intention of classifying the extracted features, early and late fusion schemes are employed, and the output scores are classified with the SVM, KNN and LDA algorithms. The evaluation of the proposed approach on the ADHD-200 testing dataset revealed that the presence of personal characteristics alone increased the classification accuracy by 3.79%. In addition, using a combination of early, late fusion and personal characteristics together improved the accuracy of the classification by 5.84%. Among the three classifiers LDA showed better results and achieved a classification accuracy of 74.93%. The comparison of results showed that the combination of early and late fusion as well as considering personal characteristics has a significant effect on enhancing classification accuracy. As a result of this, the reliability of this medical decision support system is increased.