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Autism Spectrum Disorder (ASD) remains challenging to diagnose despite its prevalence. Structural Magnetic Resonance Imaging (sMRI) has emerged as a valuable tool for such tasks. In this paper, we introduce a new multi-view deep learning-based method for ASD diagnosis based on pretrained CNN and majority voting. By combining the information from multiple views, our model can form a more complete understanding of the brain’s anatomy. The majority voting technique further boosts performance by aggregating predictions from multiple instances of the model, ensuring more reliable and accurate classifications. Our method was evaluated on the ABIDE (Autism Brain Imaging Data Exchange) dataset, achieving a notable accuracy of 98.91%.

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Enhancing Multi-view ASD Diagnosis Using Structural MRI and Pretrained CNN

  • 会议论文

  • 发表日期：

    2025年

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  • Nesrine Zemzemi,
  • Imen Hmida,
  • Nadra Ben Romdhane,
  • Emna Fendri

摘要

Autism Spectrum Disorder (ASD) remains challenging to diagnose despite its prevalence. Structural Magnetic Resonance Imaging (sMRI) has emerged as a valuable tool for such tasks. In this paper, we introduce a new multi-view deep learning-based method for ASD diagnosis based on pretrained CNN and majority voting. By combining the information from multiple views, our model can form a more complete understanding of the brain’s anatomy. The majority voting technique further boosts performance by aggregating predictions from multiple instances of the model, ensuring more reliable and accurate classifications. Our method was evaluated on the ABIDE (Autism Brain Imaging Data Exchange) dataset, achieving a notable accuracy of 98.91%.