| Utilization of deep learning to overcome the effect of ADHD ON CHILDREN |
| Paper ID : 1133-ICEEM2023 |
| Authors |
|
Eman Salah *1, Mona Shokair2, Prof. Fathi Sayed2, Wafaa A. Shalaby2 1ELECTRONIC AND COMMUNICATION DEPARTMENT, FACULTY OF Electronic ENGINEERING, MENOUFIA UNIVERSITY, EGYPT. 2Faculty of Electronic Engineering, Menoufia University |
| Abstract |
| Attention deficit hyperactivity disorder (ADHD) is a brain condition that makes it difficult for children to control their behavior. It is normal for all children to show some of these symptoms from time to time. Your child may react to stress at school or home. This research sheds light on the impact of this disorder on people with ADHD. It is important that whoever has this disease or who is helping with treatment is aware of the positive and negative effects of the disorder and its consequences. One of the types of deep learning called Convolution Neural Network (CNN) was chosen to extract the features of the images captured by Functional Magnetic Resonance Imaging (fMRI). There were three optimization methods for fMRI datasets, Nesterov-Accelerated Adaptive Moment Estimation (Nadam), stochastic gradient ratio with momentum optimizers (SGDM), and Proposed CNN. It is concluded that the accuracy of the Nadam system is 96.5%, SGDM is 95% and the proposed CNN is 98.77 which is meant that the proposed CNN is the best. |
| Keywords |
| AI, Deep learning, CNN, ADHD, fMRI |
| Status: Accepted |