ADHD Classification Based on ConvNeXt and Attention

doi:10.3969/j.issn.1006-2475.2025.07.003

Abstract

Abstract:
Abstract: Attention Deficit and Hyperactivity Disorder （ADHD）， commonly known as ADHD， is a common behavioral disorder in children. Since there is no clear etiology for ADHD， and there are only subtle differences in the brain structure between ADHD patients and normal children， which makes it difficult for clinicians to make effective diagnosis. For such disorders， a convolutional neural network based on ConvNeXt and attentional mechanisms is proposed for distinguishing ADHD patients from normal children. Firstly， the sMRI is preprocessed， secondly， the pre-trained model is loaded， the deep feature extraction is performed by the ConvNeXt network containing multidimensional collaborative attention， the ConvNeXt output layer is reconstructed and the final classification results are obtained. Validated on the ADHD-200 dataset， the experimental results show that its classification accuracy reaches 97.3%， which is better than the current mainstream methods， and the heat map of the model suggests the prefrontal lobe and other brain regions related to the disease， so it can be used as an effective and convenient auxiliary diagnosis method for ADHD.

Key words: Key words: ADHD, ConvNeXt, image classification, magnetic resonance imaging

CLC Number:

TP389.1

WANG Tao1, WU Qian1, 2. ADHD Classification Based on ConvNeXt and Attention [J]. Computer and Modernization, 2025, 0(07): 15-20.

References

［1］诸葛远莉. 正确认识儿童多动症［J］. 家庭医学（下半月）， 2023（12）: 22-24.
［2］张建昭. 儿童多动症:家庭和幼儿园如何早期发现［J］. 儿童与健康， 2023（11）: 4-5.
［3］ HAN D， FANG Y T， LUO H. A predictive model offor attention deficit hyperactivity disorder based on clinical assessment tools ［J］. Neuropsychiatr Dis Treat， 2020， 16: 1331-1337.
［4］ SRIDHAR C， BHAT S， ACHARYA U R， et al. Diagnosis of attention deficit hyperactivity disorder using imaging and signal processing techniques ［J］. Computers in Biology and Medicine， 2017，88:93-99.
［5］ WANG X K， WANG X Q， YANG X， et al. Gray matter network associated with attention in children with attention deficit hyperactivity disorder ［J］. Front Psychiatry， 2022，13:6-14.
［6］ ZOU L， ZHENG J N， MIAO C Y， et al. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI ［J］. IEEE Access， 2017，5:23626-23636.
［7］ ZHANG J J， ZHOU L P， WANG L. Subject-adaptive integration of multiple SICE brain networks with different sparsity［J］. Pattern Recognition， 2017，63:642-652.
［8］ DOU C F， ZHANG S K， WANG H P， et al. ADHD fMRI short-time analysis method for edge computing based on multi-instance learning ［J］. Journal of Systems Architecture， 2020，111:92-100.
［9］ ZHANG J J， ZHOU L P， WANG L， et al. Diffusion kernel attention network for brain disorder classification［J］. IEEE Transactions on Medical Imaging， 2022，41（10）:2814-2827.
［10］ JANG J， HWANG D. M3T: Three-dimensional medical image classifier using Multi-plane and Multi-slice Transformer［C］// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022:20686-20697.
［11］ SHI C P， WU H Y， WANG L G. A feature complementary attention network based on adaptive knowledge filtering for hyperspectral image classification ［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023，61:1-19.
［12］ LIU Z， LIN Y T， CAO Y， et al. Swin Transformer: Hierarchical vision transformer using shifted windows ［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision （ICCV）. IEEE， 2021: 9992-10002.
［13］ HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2016:770-778.
［14］ YU Y， ZHANG Y， CHENG Z Y， et al. MCA: Multidimensional collaborative attention in deep convolutional neural networks for image recognition ［J］. Engineering Applications of Artificial Intelligence， 2023，126:19-35.
［15］ DENG J， DONG W， SOCHER R， et al. ImageNet: A large-scale hierarchical image database［C］// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2009:248-255.
［16］马偲臆，刘晓薇，谢雪琴，等. 迁移学习在生物医学领域的应用［J］. 生物医学工程学进展， 2023，44（4）:347-356.
［17］ LIU Z， MAO H Z， WU C Y， et al. A ConvNet for the 2020s［C］// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022:11966-11976.
［18］ SRIVASTAVA V， BISWAS B. Manifold preserving CNN for pixel-based object labelling in images for high dimensional feature spaces ［J］. Neural Processing Letters， 2021，53（1）:607-635.
［19］ PATRO K K， ALLAM J P， SANAPALA U， et al. An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques ［J］. BMC Bioinformatics， 2023，24（1）:26-46.
［20］ DEVIKA K， MAHAPATRA D， SUBRAMANIAN R， et al. Outlier-based autism detection using longitudinal structural MRI ［J］. IEEE Access， 2022，10: 27794-27808.
［21］ GUO P， DU G Q， WEI L S， et al. Multiscale face recognition in cluttered backgrounds based on visual attention ［J］. Neurocomputing， 2022，469:65-80.
［22］ HU J， SHEN L， SUN G. Squeeze-and-excitation networks［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2018:7132-7141.
［23］ LIU M Q， PAN H Z， GE H M， et al. MS3Net: Multiscale stratified-split symmetric network with quadra-view attention for hyperspectral image classification ［J］. Signal Processing， 2023，212:69-79.
［24］ ZHOU P， XIE X Y， LIN Z C， et al. Towards understanding convergence and generalization of AdamW［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2024，46（9）: 6486-6493.
［25］ BELLEC P， CHU C， CHOUINARD-DECORTE F， et al. The Neuro Bureau ADHD-200 preprocessed repository ［J］. NeuroImage， 2017，144:275-286.
［26］ ZHOU B L， KHOSLA A， LAPEDRIZA A， et al. Learning deep features for discriminative localization ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2016:2921-2929.
［27］ HOOGMAN M， BRALTEN J， HIBAR D P， et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: Across-sectional mega-analysis ［J］. The Lancet Psychiatry， 2017，4（4）:310-319.

[1]	HOU Menghan, WEI Changfa. Construction of Depression Recognition Model Based on Multi-Feature Fusion [J]. Computer and Modernization, 2025, 0(03): 1-5.
[2]	HUANG Tingpei1, MA Lubiao1, LI Shibao2, LIU Jianhang1. Gesture Recognition Method Based on WiFi and Prototypical Network [J]. Computer and Modernization, 2024, 0(12): 34-39.
[3]	ZHAO Xiaoming, PAN Ting, LIU Weifeng. Automated Drawing Psychoanalysis Based on Image Classification [J]. Computer and Modernization, 2024, 0(08): 92-97.
[4]	XU Yue-wen1, LI Ming1, LI Li2. Image Classification of COVID-19 Based on Contrast Learning MocoV2 [J]. Computer and Modernization, 2024, 0(02): 81-87.
[5]	QIU Kai-xing, FENG Guang. A Multi-label Image Classification Model Based on Dual Feature Attention [J]. Computer and Modernization, 2023, 0(12): 41-47.
[6]	WU Tian, LIU Hai-hua, TONG Shun-yan. Image Classification Based on Deep Feedback CNN [J]. Computer and Modernization, 2023, 0(09): 82-86.
[7]	MA Guo-xiang, YANG Ling-fei, YAN Chuan-bo, ZHANG Zhi-hao, SUN Bing, WANG Xiao-rong. Ultrasonic Image Diagnosis of Hepatic Echinococcosis Based on Deep DenseNet Network [J]. Computer and Modernization, 2023, 0(09): 100-104.
[8]	QIN Zhu-yuan, WU Hao-zhong, TAN Dai-qing, HAN Ai-qing, ZANG Hao, WANG Xuan, TANG Yan. Fine-grained Identification of Maidong Based on Multi-scale ResNet Combining Attention Mechanism [J]. Computer and Modernization, 2023, 0(07): 105-111.
[9]	ZHU Jian-bo, GE Ming-feng, DONG Wen-fei. Alzheimer’s Disease Image Classification Based on Improved EfficientNet [J]. Computer and Modernization, 2023, 0(06): 56-61.
[10]	WU Zhi-ping, MA Yao-bin, TANG Wen-chao, HU Bi-wei, HU Bi-wei, LIU Ming-jia. Multispectral Image Classification Based on Context-aware and Super-pixel Post-processing [J]. Computer and Modernization, 2022, 0(12): 67-73.
[11]	HUANG Yan-hui, LAN Hai, WEI Xian. Lightweight Vision Transformer Based on Separable Structured Transformations [J]. Computer and Modernization, 2022, 0(10): 75-81.
[12]	YANG Zhen, SHAN Meng-jiao, YIN Zhi-jian, YANG Fan, LI Cui-mei. Fine-grained Image Classification via Channel Adaptive Discriminative Learning [J]. Computer and Modernization, 2022, 0(10): 68-74.
[13]	WANG Pan-hong, ZHU Chang-ming. Multi-label Image Classification Method Combined CNN and Interactive Features [J]. Computer and Modernization, 2022, 0(09): 85-92.
[14]	ZHANG Lin-peng, WANG Xi-yuan, LI Qiang. Image Classification Based on Double-pooling Feature Weighted Convolutional Neural Network [J]. Computer and Modernization, 2021, 0(11): 67-71.
[15]	LI Xiao-shuang, HAN Li-xin, LI Jing-xian, ZHOU Jing-wei. Multi-modal Music Emotion Classification Based on Optimized Residual Network [J]. Computer and Modernization, 2020, 0(12): 83-89.