Thakur Mahima, Snekhalatha U
Department of Electronics and Communication Engineering (Specialization in Biomedical Engineering), College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India.
Phys Eng Sci Med. 2022 Dec;45(4):1301-1315. doi: 10.1007/s13246-022-01196-2. Epub 2022 Nov 10.
The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), and Alzheimer's disease (AD) from Cognitively Normal (CN), and assess the results. 18F-FDG PET imaging modality for brain were procured from Alzheimer's disease neuroimaging initiative's (ADNI) repository. The ResNet50V2 model layers were utilised for feature extraction, with the final convolutional layers fine-tuned for this dataset's multi-classification objectives. Multiple metrics and feature maps were utilized to scrutinize and evaluate the model's statistical and qualitative inference. The multi-classification model achieved an overarching accuracy of 98.44% and Area under the receiver operating characteristic curve of 95% on the testing set. Feature maps aided in deducing finer aspects of the model's overall operation. This framework helped classifying from the 18F-FDG PET brain images, the subtypes of Mild Cognitive Impairment (MCI) which include EMCI, LMCI, from AD, CN groups and achieved an all-inclusive sensitivity of 94% and specificity of 95% respectively.
该研究旨在实现一个卷积神经网络框架，该框架使用脑成像的18F-FDG PET模态来检测痴呆的多个阶段，包括早期轻度认知障碍（EMCI）和晚期轻度认知障碍（LMCI），以及来自认知正常（CN）人群的阿尔茨海默病（AD），并评估结果。用于脑部的18F-FDG PET成像模态数据来自阿尔茨海默病神经影像倡议（ADNI）的数据库。利用ResNet50V2模型层进行特征提取，并针对该数据集的多分类目标对最终卷积层进行微调。使用多个指标和特征图来仔细检查和评估模型的统计和定性推断。该多分类模型在测试集上的总体准确率达到98.44%，受试者工作特征曲线下面积为95%。特征图有助于推断模型整体操作的更细微方面。该框架有助于从18F-FDG PET脑图像中对轻度认知障碍（MCI）的亚型进行分类，这些亚型包括EMCI、LMCI，以及AD、CN组，其总体敏感性分别为94%，特异性为95%。
