AliAmini93/ADHDeepNet
ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.
This project helps diagnose Attention Deficit Hyperactivity Disorder (ADHD) by analyzing raw brainwave data (EEG signals). It takes raw EEG recordings as input and outputs a classification indicating the likelihood of ADHD. This tool is designed for neuroscientists, clinical researchers, and medical professionals involved in ADHD diagnostics.
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Use this if you need an advanced deep learning model to aid in the diagnosis of ADHD using electroencephalogram (EEG) data.
Not ideal if you are looking for a diagnostic tool that does not rely on EEG data or requires minimal computational resources.
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Jun 25, 2024
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