Optimized Temporal Denoised Convolutional Autoencoder for Enhanced ADHD Classification Using fMRI Data

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects individuals across all age groups, from childhood through adulthood, and can significantly impact daily functioning. Early and accurate diagnosis of ADHD is crucial in clinical practice to ensure effective...

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Vydané v:	IEEE access Ročník 13; s. 29031 - 29045
Hlavní autori:	Begum, Zarina, Shaik, Kareemulla
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Adaptive algorithms adaptive osprey optimization Artificial intelligence Attention deficit hyperactivity disorder Autoencoders Brain modeling Classification Classification algorithms Convolutional neural networks Datasets Deep learning Diagnosis Effectiveness Electroencephalography Feature extraction fMRI images Functional magnetic resonance imaging Imputation Machine learning Magnetic resonance imaging Medical imaging Noise reduction Optimization optimized temporal denoised convolutional autoencoder Performance evaluation spatial features Training
ISSN:	2169-3536, 2169-3536
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