proposed an artificial intelligence-based solution for diagnosing ADHD in children

Kabudvand, Farzane

doi:10.22034/apj.2025.725727

proposed an artificial intelligence-based solution for diagnosing ADHD in children

Document Type : Original Research Article

Author

Farzane Kabudvand

Department of Computer Engineering, Islamic Azad University, Zanjan Branch, Zanjan, Iran

10.22034/apj.2025.725727

Abstract

In this article, a method is proposed for diagnosing children with Attention Deficit Hyperactivity Disorder (ADHD) using deep learning concepts and analyzing the correlation between segmented areas of functional brain images. The proposed method includes preprocessing medical images to remove distorted, noisy, incomplete, and problematic images, generating new functional brain images using autoencoders, which are applicable in artificial intelligence and deep learning, to assist in better analysis of medical images and address the limitations of medical image availability. It also involves segmenting images and creating separate networks to enhance the diagnostic accuracy of the condition and calculate the correlation between regions, ultimately leading to an effective diagnosis of ADHD in children. The conclusion of this study indicates that this combined method has a high capability for timely and accurate diagnosis of this condition in children and can serve as an effective tool in the field of child neurology and psychiatry.

Keywords

ADHD

deep learning

self-encoding networks

functional image segmentation

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