GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification
<b>Background:</b> Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The o...
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MDPI AG
2025-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/11/1425 |
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| author | Ebenezer Daniel Anjalie Gulati Shraya Saxena Deniz Akay Urgun Biraj Bista |
| author_facet | Ebenezer Daniel Anjalie Gulati Shraya Saxena Deniz Akay Urgun Biraj Bista |
| author_sort | Ebenezer Daniel |
| collection | DOAJ |
| description | <b>Background:</b> Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. <b>Methods:</b> In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. <b>Results:</b> The number of participants in both ASD and normal control (CN) subject groups was not statistically different (<i>p</i> = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. <b>Conclusions:</b> To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net. |
| format | Article |
| id | doaj-art-5fe3a345168241558b01d9d1f0eb07cd |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-5fe3a345168241558b01d9d1f0eb07cd2025-08-20T02:33:02ZengMDPI AGDiagnostics2075-44182025-06-011511142510.3390/diagnostics15111425GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism ClassificationEbenezer Daniel0Anjalie Gulati1Shraya Saxena2Deniz Akay Urgun3Biraj Bista4Department of Diagnostic Radiology, City of Hope National Medic and Center, Duarte, CA 91010, USADepartment of Radiology, Henry Ford Hospital, Detroit, MI 48202, USADepartment of Health and Exercise Science, La Sierra University, Riverside, CA 92505, USADepartment of Diagnostic Radiology, City of Hope National Medic and Center, Duarte, CA 91010, USADepartment of Diagnostic Radiology, City of Hope National Medic and Center, Duarte, CA 91010, USA<b>Background:</b> Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. <b>Methods:</b> In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. <b>Results:</b> The number of participants in both ASD and normal control (CN) subject groups was not statistically different (<i>p</i> = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. <b>Conclusions:</b> To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net.https://www.mdpi.com/2075-4418/15/11/1425deep learningVGG Netautism identificationABIDE datasetbrain imaging |
| spellingShingle | Ebenezer Daniel Anjalie Gulati Shraya Saxena Deniz Akay Urgun Biraj Bista GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification Diagnostics deep learning VGG Net autism identification ABIDE dataset brain imaging |
| title | GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification |
| title_full | GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification |
| title_fullStr | GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification |
| title_full_unstemmed | GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification |
| title_short | GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification |
| title_sort | gm vgg net a gray matter based deep learning network for autism classification |
| topic | deep learning VGG Net autism identification ABIDE dataset brain imaging |
| url | https://www.mdpi.com/2075-4418/15/11/1425 |
| work_keys_str_mv | AT ebenezerdaniel gmvggnetagraymatterbaseddeeplearningnetworkforautismclassification AT anjaliegulati gmvggnetagraymatterbaseddeeplearningnetworkforautismclassification AT shrayasaxena gmvggnetagraymatterbaseddeeplearningnetworkforautismclassification AT denizakayurgun gmvggnetagraymatterbaseddeeplearningnetworkforautismclassification AT birajbista gmvggnetagraymatterbaseddeeplearningnetworkforautismclassification |