Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Lim, Bing Yan; Lai, Khin Wee; Haiskin, Khairunnisa; Kulathilake, K.A.S.H.; Ong, Zhi Chao; Hum, Yan Chai; Dhanalakshmi, Samiappan; Wu, Xiang; Zuo, Xiaowei

doi:10.3389/fnagi.2022.876202

Cited by 30 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though the classification accuracy was high for all binary tasks, the generality of the proposed model was highly doubted since the dataset was too small. Lim et al ( 2022 ) tested a CNN, VGG-16, and ResNet-50 as the feature extractor to distinguish NC, AD, and MCI using MRI images. They trained the CNN from scratch and pre-trained VGG-16 and ResNet-50 on the ImageNet database.…”

Section: Methodsmentioning

confidence: 99%

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Zhang

Chuah

Lai

et al. 2023

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.

show abstract

Section: Methodsmentioning

confidence: 99%

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Zhang

Chuah

Lai

et al. 2023

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To accommodate the dimensional difference between natural and medical images, we adopt a different approach from Lim et al. [43]. A 1*1 convolution kernel is added before the backbone network.…”

Section: Methodsmentioning

confidence: 99%

“…Our pre-training weights are trained based on ImageNet [42], because the pre-trained model by ImageNet [42] has a fixed input configuration, that is, the input data needs to requires RGB images with three channels as input, while medical images often only have single channel. To accommodate the dimensional difference between natural and medical images, we adopt a different approach from Lim et al [43]. A 1*1 convolution kernel is added before the backbone network.…”

Section: Methodsmentioning

confidence: 99%

Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

Xuan

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Medical image segmentation plays a vital role in computer‐aided diagnosis and intelligent medical treatment. It can preprocess medical images to help doctors better diagnose diseases. Class activation map (CAM) is an important technology in weakly supervised segmentation, which can achieve image segmentation without pixel‐level label training. This technology can well meet the needs of medical image segmentation. However, CAM obtaining is still unperfect due to global average pooling (GAP). GAP will cause important and non‐important regions to be given equal attention during the training process. So, CAM cannot demarcate the boundary of the target regions well. In order to solve this problem, a global weighted average pooling network fusing the grayscale information of medical images is proposed. The proposed network can solve the problem that GAP has the same concern for important regions and non‐important regions of the feature map, because the different weights can be learned for different positions of the feature map before the GAP in the proposed model. At the same time, because of the grayscale difference between the tumor area and the non‐tumor area in the brain tumor image, the low‐level grayscale information of the medical image is fused with the high‐level semantic information extracted by the network to learn the weights. This operation gives full play to the advantages of feature maps of different levels. The experiment results on the popular medical image dataset BraTS2019 show that the proposed method can well improve the performance of CAM and help CAM fit the boundaries of objects. Meanwhile, in the DSC evaluation, the proposed method achieves a score of 64.1%, which is a 4.6% improvement over a recent research method.

show abstract

“…ResNet [22], VGGNet [23], and GoogLeNet [24] have also been introduced for AD diagnosis and prediction. Lim et al [25] proposed a multiclass classification method with basic CNN, VGG-16, and ResNet-50, followed by a new densely connected classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Lim et al. [25] proposed a multiclass classification method with basic CNN, VGG‐16, and ResNet‐50, followed by a new densely connected classifier.…”

Section: Introductionmentioning

confidence: 99%

A modified 3D EfficientNet for the classification of Alzheimer's disease using structural magnetic resonance images

Zheng

Gao

Huang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Structural magnetic resonance imaging (MRI) provides useful information for biomarker exploration and intelligent classification of Alzheimer's disease (AD). Machine learning and deep learning methods have been used in feature extraction and computer-aided diagnosis or prediction of AD. In this research, a new deep learning model is developed to detect or predict AD in an effective manner. A modified 3D EfficientNet with a sequentially connected 3D mobile inverted bottleneck convolution (MBConv) block is proposed to explore the multiscale characteristics of brain MR images for AD classification. The 3D MBConv block consists of depthwise convolution and a squeeze-and-excitation module to transform the input features into more compact features with fewer parameters than those in standard convolution. The proposed method was evaluated considering four classification tasks: (1) NC versus AD, (2) NC versus all MCI, (3) NC versus pMCI, and (4) sMCI versus pMCI. The proposed model achieved accuracies of 95.00%, 86.67%, and 83.33% for the classification of NC versus AD, NC versus pMCI, and sMCI versus pMCI, respectively, and exhibited a high performance in comparison with the classical networks and several existing methods. Efficient deep learning networks are powerful tools for the early diagnosis and prediction of AD.

show abstract

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Cited by 30 publications

References 21 publications

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

A modified 3D EfficientNet for the classification of Alzheimer's disease using structural magnetic resonance images

Contact Info

Product

Resources

About