A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection

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Year : 2024 | Volume :11 | Issue : 03 | Page : –
By
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Nagashree N.,

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Amarnath Patil,

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Ananya Rao G. R.,

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Sanjana R. S.,

  1. Associate Professor and HOD, Department of Computer Science and Engineering (Data Science), Sai Vidya Institute of Technology, Rajanukunte, Karnataka, India
  2. Assistant Professor, Department of Computer Science and Engineering (Data Science), Sai Vidya Institute of Technology, Rajanukunte, Karnataka, India
  3. Student, Department of Computer Science and Engineering (Data Science), Sai Vidya Institute of Technology, Rajanukunte, Karnataka, India
  4. Student, Department of Computer Science and Engineering (Data Science), Sai Vidya Institute of Technology, Rajanukunte, Karnataka, India

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In recent years, deep learning techniques have shown remarkable performance in various image analysis applications, particularly in the domain of medical image processing. Among these, image segmentation plays a critical role, as it helps in isolating and analyzing specific regions within medical images. The proposed study focuses on segmenting the corpus callosum, a vital structure in the human brain, using a novel optimization technique known as the Split and Merge algorithm, combined with the UNet architecture—a fully connected neural network widely recognized for its effectiveness in biomedical image segmentation. Autism, a neurodevelopmental disorder that affects social communication, behavior, and cognitive functioning, is typically diagnosed in children between the ages of 2 and 5. Research has identified the corpus callosum, the largest white matter structure in the brain, as a potential biomarker for detecting autism, especially through MRI scans. Structural abnormalities in the corpus callosum have been correlated with autism, making it a key focus in early diagnosis. This work introduces an automated approach to detecting autism by leveraging the Split and Merge UNet segmentation methodology. The proposed system aims to accurately segment the corpus callosum region from MRI images, facilitating the early detection of autism. By combining the precision of the Split and Merge algorithm with the robust learning capabilities of UNet, this methodology promises to offer an efficient and reliable tool for medical professionals, ultimately aiding in the timely intervention and treatment of autism.

Keywords: ASD, MRI, UNet, Segmentation, Split and Merge Classification, ABIDE.

[This article belongs to Journal of Multimedia Technology & Recent Advancements (jomtra)]

How to cite this article:
Nagashree N., Amarnath Patil, Ananya Rao G. R., Sanjana R. S.. A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection. Journal of Multimedia Technology & Recent Advancements. 2024; 11(03):-.
How to cite this URL:
Nagashree N., Amarnath Patil, Ananya Rao G. R., Sanjana R. S.. A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection. Journal of Multimedia Technology & Recent Advancements. 2024; 11(03):-. Available from: https://journals.stmjournals.com/jomtra/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 03/05/2024
Accepted 01/10/2024
Published 18/10/2024
136

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