AI SearchFine-grained characterization of alzheimer{u2019}s disease with deep learning using structural MRI and clinical data (Record no. 44789)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04244nas a2200409 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260818113838.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240229s20239999th u m tt 000 a eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12421777 |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Thesis no.DSAI-23-05 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lin Tun Naing |
| 245 10 - TITLE STATEMENT | |
| Title | Fine-grained characterization of alzheimer{u2019}s disease with deep learning using structural MRI and clinical data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 61 leaves : |
| Other physical details | ill. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Thesis ; |
| Volume/sequential designation | no. DSAI-23-05 |
| 500 ## - GENERAL NOTE | |
| General note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence, School of Engineering and Technology |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M. Sc.) - Asian Institute of Technology, 2023 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | As the longevity is increasing globally, aging and senile disease have become common natural processes. Unfortunately, one of the major health issues due to aging is dementia, with more than 60% of cases being attributed to Alzheimer{u2019}s Disease(AD). AD is a clin ical manifestation of irreversible neurological deficits and congnitive impairment which affect the daily living of a person. Patients may initially present with mild congitive impairment(MCI) which can progress over time to AD. Clinicians primarily depend on clinical findings and cognitive functional assessments for the diagnosis of MCI and AD. However, in real-world practice, acquiring clinical data involves a series of processes that demand considerable time and effort. Moreover, biomarkers such as structural magnetic resonance imaging(sMRI), cerebrospinal fluid(CSF) examination and positron emission tomography(PET) also play a supportive role in characterizing AD and other dementia diseases alongside clinical data. Nowadays, deep learning and artificial intelligence(AI) have been emerging in the medical field especially in biomedical imaging and AD classi fication using brain sMRI. Interestingly, some physical changes in the brain tissues such as atrophy in the area of hippocampus is one of the significant findings in diagnosing AD, however the whole brain may possibly include the contribution of the disease cau sation. Although there are several open source datasets available for automatic disease classification, the majority of them comprises MCI and AD patients who are diagnosed based on clinical findings and neuropsychological assessments rather than sMRI ab normalities. In addition, some previous works provide limited information regarding the data preprocessing method and there is a lack of explicit mention of the validation data whether they come from same distribution or not. This paper presents a system atic approach on data preprocessing and highlights the benefits of data preprocessing on biomedical imaging analysis. Finally, by utilizing the independent validation set, DenseNet121 achieves a classification accuracy of 63.44% for distinguishing between Normal Cognitive(NC), MCI and AD in a 3-way classification scenario. In 2-way clas sifications, the model achieves 93.49% in NC vs AD, 69.11% in NC vs MCI and 79.1% in MCI vs AD. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Alzheimer's Disease |
| General subdivision | Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Deep learning (Machine learning) |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dailey, Matthew N., |
| Relator term | Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mongkol Ekpanyapong, |
| Relator term | Co-Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chaklam Silpasuwanchai, |
| Relator term | Examination Committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dittapong Songsaeng, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | AIT Fellowship, |
| Relator term | Scholarship Donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Thesis ; |
| Volume/sequential designation | no. DSAI-23-05 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B20434">http://203.159.5.9/ait-thesis/detail.php?q=B20434</a> |
| 907 ## - LOCAL DATA ELEMENT G, LDG (RLIN) | |
| a | .b12421777 |
| b | mnait |
| c | a |
| 902 ## - LOCAL DATA ELEMENT B, LDB (RLIN) | |
| a | 250307 |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 240318 |
| First date, FD (RLIN) | m |
| -- | a |
| -- | a |
| -- | 0 |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 67-Electronic Resource |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 40-Archives |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : - |
| CREATED | CREATED : 2024-02-29 |
| RECORD Id | RECORD # : i1348798x |
| LPATRON | LPATRON : 0 |
| LCHKIN | LCHKIN : - |
| RENEWALS | # RENEWALS : 0 |
| -- | # OVERDUE : 0 |
| -- | IUSE3 : 0 |
| -- | TOT CHKOUT : 0 |
| -- | TOT RENEW : 0 |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : 30050120898878 |
| CREATED | CREATED : 2025-07-03 |
| RECORD Id | RECORD # : i13537477 |
| LPATRON | LPATRON : 0 |
| LCHKIN | LCHKIN : - |
| RENEWALS | # RENEWALS : 0 |
| -- | # OVERDUE : 0 |
| -- | IUSE3 : 0 |
| -- | TOT CHKOUT : 0 |
| -- | TOT RENEW : 0 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type | Barcode | Copy number |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Thesis no.DSAI-23-05 | 18/08/2026 | 18/08/2026 | 67-Electronic Resource | ||||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Thesis no.DSAI-23-05 | 18/08/2026 | 18/08/2026 | 40-Archives | 30050120898878 | 1 |