Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier
TheneurodevelopmentalAutismSpectrumDisorder(ASD)causesproblemsinsocial commu nication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deepConvolutionalNeuralNetwork(CNN)withDwarfMongooseoptimizedResidualNetwork(DM ResNet) is proposed for th...
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2023
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3003 https://doi.org/10.1109/ACCESS.2023.3325701. |
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| _version_ | 1862980613448925184 |
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| author | JAIN, SWETA |
| author2 | KUMAR TRIPATHY, HRUDAYA MALLIK, SAURAV QIN, HONG SHAALAN, YARA SHAALAN, KHALED |
| author2_role | author author author author author |
| author_facet | JAIN, SWETA KUMAR TRIPATHY, HRUDAYA MALLIK, SAURAV QIN, HONG SHAALAN, YARA SHAALAN, KHALED |
| author_role | author |
| dc.creator.none.fl_str_mv | JAIN, SWETA KUMAR TRIPATHY, HRUDAYA MALLIK, SAURAV QIN, HONG SHAALAN, YARA SHAALAN, KHALED |
| dc.date.none.fl_str_mv | 2023 2025-05-13T14:57:08Z 2025-05-13T14:57:08Z |
| dc.identifier.none.fl_str_mv | Jain, S. et al. (2023) “Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier,” IEEE Access, 11. 2169-3536 https://bspace.buid.ac.ae/handle/1234/3003 https://doi.org/10.1109/ACCESS.2023.3325701. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | IEEE Accessv11 (2023): 117741-117751 |
| dc.subject.none.fl_str_mv | Autism detection, MRI images, segmentation, VGG feature extraction, ResNet, dwarf mongoose optimization. |
| dc.title.none.fl_str_mv | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| dc.type.none.fl_str_mv | Article |
| description | TheneurodevelopmentalAutismSpectrumDisorder(ASD)causesproblemsinsocial commu nication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deepConvolutionalNeuralNetwork(CNN)withDwarfMongooseoptimizedResidualNetwork(DM ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM GMMsegmentsthevolumeintopredefinedcorticalandsubcorticalregions.Aftersegmentation,thefeatures are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%. |
| id | budr_f6ec521335f84b43503b4b174d8a31c4 |
| identifier_str_mv | Jain, S. et al. (2023) “Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier,” IEEE Access, 11. 2169-3536 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3003 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet ClassifierJAIN, SWETAKUMAR TRIPATHY, HRUDAYAMALLIK, SAURAVQIN, HONGSHAALAN, YARASHAALAN, KHALEDAutism detection, MRI images, segmentation, VGG feature extraction, ResNet, dwarf mongoose optimization.TheneurodevelopmentalAutismSpectrumDisorder(ASD)causesproblemsinsocial commu nication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deepConvolutionalNeuralNetwork(CNN)withDwarfMongooseoptimizedResidualNetwork(DM ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM GMMsegmentsthevolumeintopredefinedcorticalandsubcorticalregions.Aftersegmentation,thefeatures are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.IEEE2025-05-13T14:57:08Z2025-05-13T14:57:08Z2023ArticleJain, S. et al. (2023) “Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier,” IEEE Access, 11.2169-3536https://bspace.buid.ac.ae/handle/1234/3003https://doi.org/10.1109/ACCESS.2023.3325701.enIEEE Accessv11 (2023): 117741-117751oai:bspace.buid.ac.ae:1234/30032025-05-13T14:59:14Z |
| spellingShingle | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier JAIN, SWETA Autism detection, MRI images, segmentation, VGG feature extraction, ResNet, dwarf mongoose optimization. |
| title | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| title_full | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| title_fullStr | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| title_full_unstemmed | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| title_short | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| title_sort | Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier |
| topic | Autism detection, MRI images, segmentation, VGG feature extraction, ResNet, dwarf mongoose optimization. |
| url | https://bspace.buid.ac.ae/handle/1234/3003 https://doi.org/10.1109/ACCESS.2023.3325701. |