Performance of deep learning algorithms in predicting autism spectrum disorders based on fMRI data

Performance of deep learning algorithms in predicting autism spectrum disorders based on fMRI data


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دانشگاه علوم پزشکی تبریز
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نویسندگان: ناهیده قره آغاجی , صهبا مفضل

عنوان کنگره / همایش: 3rd International Iranian Radiology Students Congress , Iran (Islamic Republic) , سبزوار , 2023

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نویسنده ثبت کننده مقاله ناهیده قره آغاجی
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه دانشکده پیراپزشکی
کد مقاله 83545
عنوان فارسی مقاله Performance of deep learning algorithms in predicting autism spectrum disorders based on fMRI data
عنوان لاتین مقاله Performance of deep learning algorithms in predicting autism spectrum disorders based on fMRI data
نوع ارائه پوستر
عنوان کنگره / همایش 3rd International Iranian Radiology Students Congress
نوع کنگره / همایش بین المللی
کشور محل برگزاری کنگره/ همایش Iran (Islamic Republic)
شهر محل برگزاری کنگره/ همایش سبزوار
سال انتشار/ ارائه شمسی 1402
سال انتشار/ارائه میلادی 2023
تاریخ شمسی شروع و خاتمه کنگره/همایش 1402/07/11 الی 1402/07/12
آدرس لینک مقاله/ همایش در شبکه اینترنت
آدرس علمی (Affiliation) نویسنده متقاضی Department of Radiology, Faculty of Allied Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

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نویسنده نفر چندم مقاله
ناهیده قره آغاجیاول
صهبا مفضلدوم

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عنوان متن
کلمات کلیدیDeep learning algorithms, Autism spectrum disorder, Functional magnetic resonance imaging
خلاصه مقالهAutism spectrum disorder (ASD) is a complex neurodevelopmental disease [1]. People with autism have unusual communication and repetitive behaviours with restricted activities [2]. Various factors such as genetics, environment, and abnormal neural connectivity play a role in the pathogenesis of the disease [3]. Since only the evaluation of social behaviour and language skills in an autistic patient cannot provide information about the patient's neurological patterns, using functional magnetic resonance imaging (fMRI) enables the evaluation of the brain's functional connectivity as well as obtaining precise information for neuroscientists about Autism. Deep learning algorithms due to their features such as auto extract features of the images and capturing hidden representations can be effective in the early diagnosis of Autism [4]. The purpose of this study was to investigate the performance of deep learning algorithms in predicting ASD using fMRI data. We used scientific databases such as Google Scholar, PubMed, and Web of Science to search keywords “deep learning algorithms”, “autism spectrum disorder”, and “functional magnetic resonance imaging”. Then, we extracted the related articles and reviewed them. The obtained results indicated that various deep learning algorithms such as Conditional Generative Adversarial Network (cGAN), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Deep Q Network (DQN) were used for ASD prediction using resting state fMRI data. also, the accuracy and sensitivity of these approaches were determined in the range of (64-97%) and (79-90%), respectively. It can be concluded that deep learning algorithms indicate a diagnostic performance to predict ASD using resting state fMRI data.

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Performance of deep learning.JPG1402/09/28132630دانلود
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