SNDT WOMEN'S UNIVERSITY
BMK Knowledge Resource Centre
Vithaldas Vidyavihar, Juhu Tara Road,
Santacruz (West) Mumbai - 400049
Synergizing fMRI Connectivity and Deep Learning for Precise Schizophrenia Diagnosis
Rajveer Singh Lalawat
Synergizing fMRI Connectivity and Deep Learning for Precise Schizophrenia Diagnosis - p1630-1643
Schizophrenia (SZ) is a multifaceted neurological disorder influenced by various factors, including brain chemistry, genetics, birth complications, and delusions. Functional Magnetic Resonance Imaging (fMRI) serves as a valuable technique for SZ detection, capturing subtle changes in blood flow associated with brain activity. However, manual screening of fMRI scans for SZ is susceptible to errors, time-intensive, and complicated by image contamination. In this study, we introduce a pioneering method for SZ diagnosis that integrates level analysis, seed-based voxel activation, and adaptive statistical parametric mapping techniques. This approach supersedes traditional methods by incorporating pre-processing and voxel activation into fMRI images, enhancing intensities and enabling the identification of brain networks or regions with significant effects at a group level, thus improving model accuracy. We leverage pre-trained ImageNet dataset deep learning models (DLMs) such as VGG- 16, ResNet50, MobileNet, and a newly developed simplified DLM named SZ-Net. Our findings demonstrate that SZ-Net achieves an impressive 10-fold validation classification accuracy of 99.24%, highlighting its proficiency in accurately categorizing fMRI scans. Additionally, SZ-Net requires fewer learnable parameters compared to pre-trained models, resulting in a more efficient and compact architecture. The proposed system holds promise for enhancing the performance of medical instruments, particularly in SZ detection using fMRI images.
Schizophrenia
Deep learning
Statistical parametric mapping (SPM)
Functional magnetic resonance imaging (fMRI)
Synergizing fMRI Connectivity and Deep Learning for Precise Schizophrenia Diagnosis - p1630-1643
Schizophrenia (SZ) is a multifaceted neurological disorder influenced by various factors, including brain chemistry, genetics, birth complications, and delusions. Functional Magnetic Resonance Imaging (fMRI) serves as a valuable technique for SZ detection, capturing subtle changes in blood flow associated with brain activity. However, manual screening of fMRI scans for SZ is susceptible to errors, time-intensive, and complicated by image contamination. In this study, we introduce a pioneering method for SZ diagnosis that integrates level analysis, seed-based voxel activation, and adaptive statistical parametric mapping techniques. This approach supersedes traditional methods by incorporating pre-processing and voxel activation into fMRI images, enhancing intensities and enabling the identification of brain networks or regions with significant effects at a group level, thus improving model accuracy. We leverage pre-trained ImageNet dataset deep learning models (DLMs) such as VGG- 16, ResNet50, MobileNet, and a newly developed simplified DLM named SZ-Net. Our findings demonstrate that SZ-Net achieves an impressive 10-fold validation classification accuracy of 99.24%, highlighting its proficiency in accurately categorizing fMRI scans. Additionally, SZ-Net requires fewer learnable parameters compared to pre-trained models, resulting in a more efficient and compact architecture. The proposed system holds promise for enhancing the performance of medical instruments, particularly in SZ detection using fMRI images.
Schizophrenia
Deep learning
Statistical parametric mapping (SPM)
Functional magnetic resonance imaging (fMRI)