Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification

Hu, Yuhuan; Wen, Caiyun; Cao, Guoquan; Wang, Jingqiang; Feng, Yuanjing

doi:10.1016/j.neulet.2022.136673

Cited by 9 publications

(9 citation statements)

References 30 publications

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“…Twenty-six shape-based and intensity-based features can be extracted for all extraction settings (width, filter, bin, and contour). With sixteen distinct stages, which can be permutations of 2 contours (HGG and Gold), bandwidth (2,4,8,16), and two filters (LoG and original) and the entire count of derived radionics imaging features was 1450.…”

Section: Radiomics Feature Extractionmentioning

confidence: 99%

“…The following equation is utilized for mathematically modeling these behaviors as follows: Whereas represents the best position of the attained solution, characterizes the count of iterations, and the constant variables and are characterized by: (6) (7) Now, denotes an arbitrary vector amongst [0, 1], and variable is an arbitrary vector utilized for controlling the convergence procedure. It can be linearly decreased from two to zero through iteration: (8) In Eq. ( 5), indicates the present iteration, and shows the maximal iteration count of the vector utilized for displaying the transition method amongst the exploratory and exploitative actions.…”

Section: E Hyperparameter Tuningmentioning

confidence: 99%

“…To conquer this problem, deep learning (DL), an emerging field of ML study that uses raw neuroimaging information to generate features through "on-the-fly" learning, is received significant interest in the Area of large-scale, higher-dimension medical imaging analysis. DL approaches, like convolution neural networks (CNN), have proved to outperform the present ML method [8,9]. Compared with conventional ML techniques, the DL approach automatically discovers the proper representation without specialized knowledge and permits the non-expert to utilize DL approaches efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

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Brain disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities including Positron Emission Tomography (PET), Structural Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, Graph CNN, Modified ResNet18, Non-linear SVM, ResNet50-SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.

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Section: Radiomics Feature Extractionmentioning

confidence: 99%

Section: E Hyperparameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

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“…e characteristics of left cerebellum 6 (99), left postcentral gyrus (57), right inferior frontal gyrus, opercular part (12), right cerebellum 6 (100), and vermis 6 (112) positively correlated with outcomes using fALFF datasets. e characteristics of the right middle temporal gyrus (86), left middle temporal gyrus (85), left temporal pole: middle temporal gyrus (87), right cerebellum 7b (102), and right olfactory cortex (22) positively correlated with outcomes using mPerAF datasets. e features of the left cerebellum 10 (107), right middle temporal gyrus (86), right inferior frontal gyrus, opercular part (12), vermis 6 (112), and right amygdala (42) positively correlated with outcomes using PerAF datasets.…”

Section: Model Interpretation: Shapley Additive Explanations (Shap) A...mentioning

confidence: 99%

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

et al. 2022

Computational Intelligence and Neuroscience

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The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.

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“…Inik and Turan, 2018) and shorten the early diagnosis process by inferring from images in the diagnosis of multi-class diseases, such as brain tumors. Deep learning methods are widely used in many medical classification problems, such as the classification of dermatological diseases (Zhou et al, 2022), cardiovascular diseases (Li et al, 2023), Alzheimer's disease (Hu et al, 2022), Parkinson's disease (Rezaee et al, 2022), chest diseases (Ibrahim et al, 2021), colon cancer and diseases (Pacal et al, 2020;Pacal and Karaboga, 2021) breast cancer (İ. Pacal, 2022), and brain tumors (Jia and Chen, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

ÇELİK

İni̇k

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

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Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification

Cited by 9 publications

References 30 publications

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

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