scholarly journals Differentiation of Alzheimer Conditions in MR Brain Images Using a Single Inception Module Network

2021 ◽  
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Input Image ◽  
Mr Images ◽  
Brain Images ◽  
Medical Expertise ◽  
Module Network ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Normal Controls

In this study, an attempt has been made to differentiate Alzheimer’s Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network. The performance of the model is computed and analyzed for different input image sizes. Results show that the single inception module is able to classify AD stages using MR images. The end-to-end network differentiates AD from NC with 85% precision. The model is found to be effective for varied sizes of input images. Since the proposed approach is able to categorize AD stages, single inception module networks could be used for the automated AD diagnosis with minimum medical expertise.

Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahana Priyanka ◽  
Kavitha Ganesan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Network Models ◽  
Curve Evolution ◽  
Neural Network Models ◽  
Brain Images ◽  
Whole Brain ◽  
Shape Constraint ◽  
Mr Brain Images ◽  
Trained Neural Network

Abstract The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

scholarly journals Interpolation of the Histogramed MR Brain Images for Resolution Enhancement

2019 ◽  
Vol 8 (11) ◽  
pp. 1253-1256 ◽  
Keyword(s):  
Performance Metrics ◽  
Interpolation Method ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
The Body ◽  
Mr Images ◽  
Brain Images ◽  
Method Performance ◽  
Mr Brain Images ◽  
Mr Brain

Magnetic resonance imaging (MRI) is an incredible testing method which provides appropriate anatomical images of the body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. However, with the measured MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. Usually to improve the resolution of the MR image, histogram equalization process has to be applied. In this paper, interpolation method is applied to improve the resolution of MR brain images for the histogram-ed images. And for the assessment of the skillfulness of introduced method, performance metrics such as peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE) are measured. The peak of signal for the enhanced images through interpolation will be much better and may have the good variation to the mean brightness error. With this there can be potential to the artificial intelligence for better diagnosis in complex decisive instances

scholarly journals Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

2021 ◽  
Vol 7 (2) ◽  
pp. 763-766
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Network Performance ◽  
Neurodegenerative Disorder ◽  
Automated Analysis ◽  
Brain Regions ◽  
Mr Images ◽  
Residual Network ◽  
Brain Images ◽  
Precise Diagnosis ◽  
Mr Brain Images ◽  
Mr Brain

Abstract Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

Study on Texture Features of Cingulum in Different Gender Patients with Alzheimer’s Disease and Mild Cognitive Impairment Based on MR Images

2011 ◽  
Vol 301-303 ◽  
pp. 1060-1065 ◽  
Author(s):  
Xu Wang ◽  
Long Zheng Tong ◽  
Xin Li ◽  
Xiao Xia Zhou ◽  
Hui Fang Yang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Pathological Changes ◽  
Mr Images ◽  
Occurrence Matrix ◽  
Normal Controls

The aim of this article is to study the texture features of cingulum in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on magnetic resonance images, and explore the texture differences derived from different gender among each group. Texture analysis was performed on 7 AD patients, 14 MCI patients and 11 normal controls (NC). Texture features extracted from gray level co-occurrence matrix and run-length matrix were analyzed between each two groups. The results showed that texture features of the anterior cingulum had significant differences in the multiple comparisons and features of the posterior cingulum had significant differences between AD and MCI group as well as AD and NC group. There were significant differences between AD and MCI group as well as AD and NC group in male’s cingulum. While in female’s cingulaum, the differences were founded between AD and NC group. The results indicated that the pathological changes in cingulum could be reflected by texture features and the pathological changes may be different in the two genders.

scholarly journals Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2881-2887
Keyword(s):  
Fuzzy Clustering ◽  
Bias Field ◽  
Mr Images ◽  
Brain Images ◽  
Normal Tissues ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Abnormal Tissue

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

scholarly journals CLUSTERING MULTIPLE SCLEROSIS SUBGROUPS WITH MULTIFRACTAL METHODS AND SELF-ORGANIZING MAP ALGORITHM

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740001 ◽  
Author(s):  
YELIZ KARACA ◽  
CARLO CATTANI
Keyword(s):  
Multiple Sclerosis ◽  
Self Organizing Map ◽  
Mr Images ◽  
Brain Images ◽  
Som Algorithm ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Brain ◽  
Multifractal Method ◽  
Self Organizing

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.

2021 ◽  
Vol 17 ◽  
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu
Keyword(s):  
Brain Tumor ◽  
Early Detection ◽  
Hybrid Model ◽  
High Performance ◽  
Tumor Detection ◽  
Classification Model ◽  
Mr Images ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Objective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best Accuracy. Materials: we trained and validated our model using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. Results and Conclusions: The novelty of our hybrid CNN-DWA model showed the best results and high performance with Accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the Tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Subjective Spatial Navigation Complaints - A Frequent Symptom Reported by Patients with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease

2018 ◽  
Vol 15 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Jiri Cerman ◽  
Ross Andel ◽  
Jan Laczo ◽  
Martin Vyhnalek ◽  
Zuzana Nedelska ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Spatial Navigation ◽  
Subjective Cognitive Decline ◽  
Great Effort ◽  
Frequent Symptom ◽  
Normal Controls

Background: Great effort has been put into developing simple and feasible tools capable to detect Alzheimer's disease (AD) in its early clinical stage. Spatial navigation impairment occurs very early in AD and is detectable even in the stage of mild cognitive impairment (MCI). Objective: The aim was to describe the frequency of self-reported spatial navigation complaints in patients with subjective cognitive decline (SCD), amnestic and non-amnestic MCI (aMCI, naMCI) and AD dementia and to assess whether a simple questionnaire based on these complaints may be used to detect early AD. Method: In total 184 subjects: patients with aMCI (n=61), naMCI (n=27), SCD (n=63), dementia due to AD (n=20) and normal controls (n=13) were recruited. The subjects underwent neuropsychological examination and were administered a questionnaire addressing spatial navigation complaints. Responses to the 15 items questionnaire were scaled into four categories (no, minor, moderate and major complaints). Results: 55% of patients with aMCI, 64% with naMCI, 68% with SCD and 72% with AD complained about their spatial navigation. 38-61% of these complaints were moderate or major. Only 33% normal controls expressed complaints and none was ranked as moderate or major. The SCD, aMCI and AD dementia patients were more likely to express complaints than normal controls (p's<0.050) after adjusting for age, education, sex, depressive symptoms (OR for SCD=4.00, aMCI=3.90, AD dementia=7.02) or anxiety (OR for SCD=3.59, aMCI=3.64, AD dementia=6.41). Conclusion: Spatial navigation complaints are a frequent symptom not only in AD, but also in SCD and aMCI and can potentially be detected by a simple and inexpensive questionnaire.

scholarly journals Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Pyo Kim ◽  
Jonghoon Kim ◽  
Hyemin Jang ◽  
Jaeho Kim ◽  
Sung Hoon Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apoe Genotype ◽  
Texture Features ◽  
Structural Mri ◽  
Regularized Regression ◽  
Mr Images ◽  
Baseline Model ◽  
Imaging Model ◽  
T1 And T2

AbstractPredicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

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