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Synergizing fMRI Connectivity and Deep Learning for Precise Schizophrenia Diagnosis (Record no. 133172)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02085nam a2200145 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251101b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rajveer Singh Lalawat |
| 245 ## - TITLE STATEMENT | |
| Title | Synergizing fMRI Connectivity and Deep Learning for Precise Schizophrenia Diagnosis |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p1630-1643 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | 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. |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Schizophrenia">Schizophrenia</a> |
| -- | <a href="Deep learning">Deep learning</a> |
| -- | <a href="Statistical parametric mapping (SPM)">Statistical parametric mapping (SPM)</a> |
| -- | <a href="Functional magnetic resonance imaging (fMRI)">Functional magnetic resonance imaging (fMRI)</a> |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 80269 |
| Host Itemnumber | 114212 |
| Place, publisher, and date of publication | New Delhi IETE |
| Title | IETE Journal of Research |
| International Standard Serial Number | 0377-2063 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 01/11/2025 | JP976.13 | 01/11/2025 | 01/11/2025 | Journal Article |