Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
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%.
Description
Keywords
Autism detection, MRI images, segmentation, VGG feature extraction, ResNet, dwarf
mongoose optimization.
Citation
Jain, S. et al. (2023) “Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier,” IEEE Access, 11.