干旱区生态环境知识资源中心(Arid): A slice selection guided deep integrated pipeline for Alzheimer's prediction from Structural Brain MRI

Arid

DOI	10.1016/j.bspc.2023.105773
	A slice selection guided deep integrated pipeline for Alzheimer's prediction from Structural Brain MRI
	Inan, Muhammad Sakib Khan; Sworna, Nabila Sabrin; Islam, A. K. M. Muzahidul; Islam, Salekul; Alom, Zulfikar; Azim, Mohammad Abdul; Shatabda, Swakkhar
通讯作者	Shatabda, S
来源期刊	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
EISSN	1746-8108
出版年	2024
卷号	89
英文摘要	Alzheimer's disease, a progressive form of dementia, has risen to become the fifth leading cause of death among individuals aged 65 and older. The diagnosis of Alzheimer's is both time-consuming and costly, involving radiologists and clinical experts at multiple stages, which presents a significant challenge in the medical field. Moreover, cases of Alzheimer's and dementia often go undiagnosed or misdiagnosed worldwide. To address this issue, medical experts meticulously analyze patients' structural MRI (sMRI) scans to identify potential abnormalities linked to Alzheimer's or other forms of dementia. Recognizing the devastating impact of this disease on people's lives, Artificial Intelligence (AI) researchers have been dedicated to developing automated solutions for early-stage Alzheimer's diagnosis in recent years, aiming to support medical practitioners in their efforts. Despite the application of various AI-driven solutions that use sMRI data for Alzheimer's diagnosis, there are still research gaps that need attention. These gaps include the need for guided slice selection and the development of a simpler yet effective integrated pipeline where each stage of the process is fully automated, eliminating the need for medical practitioner intervention. In this study, we propose an integrated automated solution that incorporates a guided machine learning-based selection process using K-Means++ leading to a Gradient Boosting-based method for identifying the 16 most relevant 2-dimensional sMRI slices from 3dimensional sMRI data. This step is crucial for accurate Alzheimer's classification. Furthermore, we introduce a deep learning architecture that combines EfficientNetV2S-based transfer learning with densely-learned features in an optimized manner. To evaluate the effectiveness of our proposed deep-integrated architecture, we used two benchmark datasets from ADNI and OASIS, conducting rigorous experimental analysis and validation. The results demonstrated that our integrated architecture outperformed all other experimented architectures, achieving a 20-Fold Cross Validation Accuracy of 83.64% (CN vs AD), 82.69% (CN vs MCIc), and 71.40% (CN vs MCInc) on the ADNI dataset, and 91.54% (CN vs AD) on the OASIS dataset. This signifies the potential of our approach in improving Alzheimer's diagnosis accuracy and offers hope for early detection and intervention in this debilitating disease.
英文关键词	Alzheimer's MRI Slice selection Transfer learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001125734600001
WOS关键词	DISEASE ; DIAGNOSIS ; BIOMARKERS ; CLASSIFICATION ; NETWORKS ; DEMENTIA ; MEMORY
WOS类目	Engineering, Biomedical
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403031
推荐引用方式 GB/T 7714	Inan, Muhammad Sakib Khan,Sworna, Nabila Sabrin,Islam, A. K. M. Muzahidul,et al. A slice selection guided deep integrated pipeline for Alzheimer's prediction from Structural Brain MRI[J],2024,89.
APA	Inan, Muhammad Sakib Khan.,Sworna, Nabila Sabrin.,Islam, A. K. M. Muzahidul.,Islam, Salekul.,Alom, Zulfikar.,...&Shatabda, Swakkhar.(2024).A slice selection guided deep integrated pipeline for Alzheimer's prediction from Structural Brain MRI.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,89.
MLA	Inan, Muhammad Sakib Khan,et al."A slice selection guided deep integrated pipeline for Alzheimer's prediction from Structural Brain MRI".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 89(2024).