مروری بر روش‌های یادگیری عمیق در تشخیص اختلال نقص توجه - بیش فعالی
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسیاکرم فیضی 1 , سیدعابد حسینی 2 , محبوبه هوشمند 3 *
1 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
2 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
3 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
کلید واژه: اختلال نقص توجه/بیش فعالی, سیگنال های مغزی, تصاویر تشدید مغناطیسی, یادگیری عمیق,
چکیده مقاله :
اختلال نقص توجه/بیش‌فعالی (ADHD) یکی از شایع‌ترین اختلال¬های رفتاری در کودکان و نوجوانان است. تشخیص دقیق و به‌موقع آن برای آغاز درمان مناسب حائز اهمیّت است. در سال‌های اخیر با پیشرفت در زمینههای یادگیری ماشین و یادگیری عمیق، پژوهشگران توجه ویژه‌ای به تشخیص بهتر ADHD داشته‌اند. این پژوهش به بررسی کاربردهای یادگیری عمیق در تشخیص ADHD از طریق تحلیل داده‌های مختلف ازجمله سیگنال‌های الکتروانسفالوگرافی و تصاویر تشدید مغناطیسی می‌پردازد. همچنین چالش‌ها و محدودیّت‌های موجود در این زمینه را موردبررسی قرار می‌دهد. هدف این پژوهش ارائه یک دیدگاه جامع در مورد پتانسیل استفاده از یادگیری عمیق در تشخیص ADHD و مسیرهای آتی پژوهش در این حوزه است. امید است که این پژوهش بتواند به‌عنوان راهنمایی کارآمد، مسیر پژوهشگران این حوزه را هموار سازد.
Attention deficit/hyperactivity disorder (ADHD) is one of the most common behavioral disorders in children and adolescents. Accurate and timely diagnosis is important to start appropriate treatment. In recent years, with the progress in the fields of machine learning and deep learning, researchers have paid special attention to better diagnosis of ADHD. This research examines the applications of deep learning in the diagnosis of ADHD through the analysis of various data such as electroencephalography signals and magnetic resonance images. It also examines the challenges and limitations in this field. The aim of this research is to provide a comprehensive perspective on the potential of using deep learning in the diagnosis of ADHD and the future directions of research in this field. It is hoped that this research can pave the way for researchers in this field as an efficient guide.
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