Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3611
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dc.contributor.authorMishra, Mayank-
dc.contributor.authorPati, Umesh C-
dc.date.accessioned2022-01-05T06:03:11Z-
dc.date.available2022-01-05T06:03:11Z-
dc.date.issued2021-12-
dc.identifier.citationInternational Conference on Advanced Network Technologies and Intelligent Computing (ANTIC-2021)en_US
dc.identifier.urihttp://hdl.handle.net/2080/3611-
dc.descriptionCopyright of this paper is with proceedings publisheren_US
dc.description.abstractBrain imaging has played a very crucial role in the detection of various brain disorders. Among many brain imaging modalities, Magnetic Resonance Imaging (MRI) has proven its importance due to its detailed information regarding the insight of the brain. Autism Spectrum Disorder (ASD) has emerged as a very serious brain disorder due to its late detection among people. It comprises symptoms that are generally ignored, and this creates the urgency for its early detection. This work puts forward the method for the detection of ASD utilizing Machine Learning (ML) with the features extracted from sMRI (Structural Magnetic Resonance Imaging). Surface morphometric and volumetric morphometric features have been utilized for training the machine learning models. The cross-validation approach has been used to avoid overfitting problem occurred during training and testing steps. Machine learning models such as Random Forest (RF), Extra Trees (ET), Linear Support Vector Machine(SVM), Non - Linear SVM, and K- Nearest Neighbors (KNN) have been used for classification between ASD and controls. To evaluate the performance of classification, accuracy, precision, recall, and ROC- AUC score values have been considered.en_US
dc.language.isoenen_US
dc.subjectAutism, Brain Imaging, Random Forest,en_US
dc.subjectSupport Vector Machine, sMRen_US
dc.titleAutism detection using surface and volumetric morphometric feature of sMRI with Machine learning approachen_US
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