3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes

Year 2023, Volume: 25 Issue: 73, 1 - 8, 26.01.2023

Gürcan Taşpınar Nalan Özkurt

https://doi.org/10.21205/deufmd.2023257301

Cited By: 2

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders and it is threatening especially to the academic performance of children. Its neurobiological diagnosis is essential for clinicians to treat ADHD patients properly. Along with machine learning algorithms, and neuroimaging technologies, especially functional magnetic resonance imaging is increasingly used as biomarker in attention deficit hyperactivity disorder. Also, machine learning methods have been becoming popular at last times. This study presents an optimized 3-dimensional convolutional neural network to classify functional magnetic resonance imaging volumes into two classes to assist experts in diagnosing ADHD. To demonstrate the importance of extracting 3D relationships of data, the method has been tested on ADHD-200 public datasets and its performance on the hold-out testing datasets has been evaluated. Then the network performance has been compared with several recent ADHD detection convolutional neural networks in the literature. It has been observed that the proposed network has a promising performance.

Keywords

Attention Deficit Hyperactivity Disorder, Functional Magnetic Resonance Imaging, 3D Convolutional Neural Network, ADHD-200 Public Datasets

fMRG Hacimlerini Kullanarak DEHB’nin 3B ESA Tabanlı Otomatik Teşhisi

Abstract

Dikkat eksikliği hiperaktivite bozukluğu (DEHB) en sık görülen beyin deformasyonlarından biridir ve özellikle çocukların okul başarılarını olumsuz yönde etkilemektedir. Uzmanların, DEHB hastalarına uygun tedavi verebilmeleri için bu hastalığın nörobiyolojik tanısı önemlidir. Makine öğrenimi algoritmaları ile birlikte, nörogörüntüleme teknolojileri, özellikle fonksiyonel manyetik rezonans görüntüleme, dikkat eksikliği hiperaktivite bozukluğunda biyobelirteç olarak giderek daha fazla kullanılmaktadır. Ayrıca, makine öğrenme yöntemleri son zamanlarda popüler hale gelmektedir. Bu çalışma ile, DEHB tanısında uzmanlara yardımcı olmak amacıyla fonksiyonel manyetik rezonans görüntüleme hacimlerini iki sınıfa ayırmak için optimize edilmiş 3 boyutlu evrişimli bir sinir ağı sunulmaktadır. Verilerin 3 boyutlu ilişkilerinin çıkarılmasının önemini göstermek için yöntem, halka açık ADHD-200 veri setlerinin öğrenme ve test verileri kullanılarak test edilmiş ve sinir ağının performansı değerlendirilmiştir. Daha sonra sinir ağının performansı, literatürdeki birkaç yeni DEHB algılama evrişimli sinir ağı ile karşılaştırılmıştır. Kullanılan sinir ağının umut verici bir performansa sahip olduğu gözlemlenmektedir.

Keywords

Dikkat Eksikliği Hiperaktivite Bozukluğu, fonksiyonel manyetik rezonans görüntüleme, 3B Evrişimsel Sinir Ağı, Halka Açık ADHD-200 Veri Setleri

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