Alzheimer's disease detection using depthwise separable convolutional neural networks

Liu, Junxiu; Li, Mingxing; Luo, Yuling; Yang, Su; Li, Wei; Bi, Yifei

doi:10.1016/j.cmpb.2021.106032

Cited by 64 publications

(12 citation statements)

References 46 publications

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“…According to the data in Table 6, which represents a comparison between our proposed research and the previous research, our research considered as the first research that utilized cerebral blood flow biomarker from DSA scans for Alzheimer's disease detection. Moreover, it has outperformed the previous works [52][53][54][55][56][57]60,61,[63][64][65][66][67] in terms of accuracy with a 96.7% score. To guarantee a fair comparison, the researchers in [61,65,66] have utilized 3D an 4D scans in their methodologies, while our proposed methodology utilized 2D scans of distinct modality.…”

Section: Comparison With Previous Workmentioning

confidence: 76%

“…Lately, Liu et al [67] used MRI scans for 30 patients with Alzheimer's disease and 332 normal subjects from the OASIS database, and they applied resampling techniques on the data set, where the final form contained scans for 450 patients with Alzheimer's disease and 532 normal subjects. Besides, a number of MRI scans from the ADNI database were utilized for testing their model.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

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Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh

Almahmoud

Ali

et al. 2021

BDCC

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Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.

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Section: Comparison With Previous Workmentioning

confidence: 76%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh

Almahmoud

Ali

et al. 2021

BDCC

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“…A portion of their dataset was used as source and the rest as target dataset. More than 70% of the studies utilized existing competitive algorithms such as VGG (Simonyan and Zisserman, 2015), AlexNet (Krizhevsky et al, 2017), ResNet (He et al, 2016;Ni et al, 2021), Inception (Szegedy et al, 2015;Liu et al, 2021). VGG was the most commonly used algorithm among existing algorithms (excluding custom CNNs) mainly because VGG is already pre-trained on a large-scale dataset (ImageNet) and had strong performance on different problems including medical image processing (Gao et al, 2019a).…”

Section: Cnn-based Algorithms For Transfer Learningmentioning

confidence: 99%

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.

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“…Using the concept of depthwise separable CNN, a novel approach for AD classification is proposed by Junxiu Liu, et al [38]. The authors have claimed that, a small set of MR images are acquired for training and testing and still achieved a high classification performances.…”

Section: Related Study: Ann In Classification Of Ad Using Brain Imagesmentioning

confidence: 99%

An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images

et al. 2021

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Alzheimer's Disease (AD) is a psychological disorder in elderly people which causes severe intellectual disabilities. Proper processing of neuro-images can provide differences in brain tissues which may help in diagnosing the disease more effectively. But, due to the complex structures, this is a challenge in differentiating the brain tissues and classifying AD using traditional classification mechanisms. Deep Neural Network (DNN) is a machine learning technique that has the ability to absorb the most important information for classifying an object accurately. LeNet is a popular DNN based model with a simple and effective architecture that also consumes very less implementation time. As like most of the DNN models, LeNet also uses MaxPooling layer for dimensionality reduction by eliminating the information of minimum valued elements. In brain images low intensity valued pixels also may contain very important features. To keep the minimum valued elements too in the network, we have created a separate layer that performs Min-Pooling operation. MinPooling and MaxPooling layers are then concatenated together. Finally, we have replaced all MaxPooling Layers in LeNet by the concatenated layers. We have analysed and compared the performances of modified LeNet model with 20 other most commonly used DNN models, and some of the related works. It is observed that, the modified LeNet model achieved the highest performances. It is also observed that, original LeNet model can classify AD with a performance rate of 80%, whereas, the proposed modified LeNet model achieved an average performance rate of 96.64%.

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Alzheimer's disease detection using depthwise separable convolutional neural networks

Cited by 64 publications

References 46 publications

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images

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