Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2025-02, Vol.194, p.105707, Article 105707
• Main Authors: Abu-Doleh, Anas, Abu-Qasmieh, Isam F., Al-Quran, Hiam H., Masad, Ihssan S., Banyissa, Lamis R., Ahmad, Marwa Alhaj
• Format: Article
• Language: English
• Subjects: Adolescent; Algorithms; Autism Spectrum Disorder; Autism Spectrum Disorder - diagnostic imaging; Autistic Disorder - diagnostic imaging; Autoencoder; Brain - diagnostic imaging; Brain - pathology; Brain MRI; Child; Female; Humans; Imaging, Three-Dimensional - methods; Machine learning; Magnetic Resonance Imaging; Male; Neural Networks, Computer; Neuroimaging - methods; Siamese Convolutional Neural Network; Subcortical regions
• ISSN: 1386-5056; 1872-8243; 1872-8243
• DOI: 10.1016/j.ijmedinf.2024.105707

•Using a 3D autoencoder for unsupervised learning to extract relevant features from subcortical structures.•Applying feature selection algorithms to identify significant regions in the subcortical structures for ASD diagnosis.•Employing a Siamese Convolutional Neural Network (SCNN) for classification, as SCNN is effective in similarity learning.•Integrating feature extraction and classification methods offers a promising technique for accurate ASD diagnosis. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interactions and behavior. Accurate and early diagnosis of ASD is still challenging even with the improvements in neuroimaging technology and machine learning algorithms. It’s challenging because of the wide range of symptoms, delayed appearance of symptoms, and the subjective nature of diagnosis. In this study, the aim is to enhance ASD recognition by focusing on brain subcortical regions, which are critical for understanding ASD pathology. First, subcortical structures were extracted from a collection of brain MRI datasets using sophisticated processing steps. Next, a 3D autoencoder was trained on these 3D images to help identify brain regions related to ASD. Two distinct feature selection methods were then applied to the features extracted from the encoder. The highest-ranked features were iteratively selected and increased to reconstruct a specific percentage of the brain that represents the most relevant parts for ASD. Finally, a Siamese Convolutional Neural Network (SCNN) was employed as the classifier model. The 3D autoencoder stage helped in identifying and reconstructing the significant subcortical regions related to ASD. Based on the studied dataset, high agreement in regions like the Putamen and Pallidum indicated the critical nature of these structures in distinguishing Autism from controls cases. Subsequently, applying SCNN on these selected subcortical regions yielded promising results. For example, using the classifier on the output regions identified by the Mutual Information (MI) features selection method achieved the highest accuracy of 0.66. This study shows that using a two-stage model involving autoencoder and SCNN can notably improve the classification of ASD from brain MRI volumetric images. Applying an iterative feature extraction approach allowed to achieve a more accurate identification of ASD-related brain areas. This two-stage approach not only improved classification performance but also en