Age Detection from Brain MRI Images Using the Deep Learning

Siar, Masoumeh; Teshnehlab, Mohammad

doi:10.1109/iccke48569.2019.8964911

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5. In this subsection, the efficacy of the proposed KRR-RVFL model with various kernel functions like the polynomial kernel (PK), gaussian kernel (GK), is compared with standard RVFL, deep RVFL (dRVFL), ensemble dRVFL (edRVFL) and different classifiers like softmax [31], SVM [22], Random Forest (RF) [27], Ensemble Bagging (EB) [25], K-Nearest Neighbor (KNN) [5], Naive Bayes (NB) [37]. The performance matrices of various classifiers about accuracy, recall, specificity, precision, and F1-score are tabulated in Table III…”

Section: Comparison Of Different Segmented Brain Tissue Volumesmentioning

confidence: 99%

See 1 more Smart Citation

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation

Pilli,

Goel,

Murugan

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

Accelerated brain aging and abnormalities are associated with variations in brain patterns. Effective and reliable assessment methods are required to accurately classify and estimate brain age. In this study, a brain age classification and estimation framework is proposed using structural magnetic resonance imaging (sMRI) scans, a 3D convolutional neural network (3D-CNN), and a kernel ridge regression-based random vector functional link (KRR-RVFL) network. We used 480 brain MRI images from the publicly available IXI database and segmented them into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images to show age-related associations by region. Features from MRI images are extracted using 3D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. The proposed algorithm achieved high classification accuracy, 97.22%, 99.31%, and 95.83% for GM, WM, and CSF regions, respectively. Moreover, the proposed algorithm demonstrated excellent prediction accuracy with a mean absolute error (MAE) of 3.89 years, 3.64 years, and 4.49 years for GM, WM, and CSF regions, confirming that changes in WM volume are significantly associated with normal brain aging. Additionally, voxel-based morphometry (VBM) examines age-related anatomical alterations in different brain regions in GM, WM, and CSF tissue volumes.

show abstract

Section: Comparison Of Different Segmented Brain Tissue Volumesmentioning

confidence: 99%

“…The authors achieved higher prediction accuracy: mean absolute error = 3.77 years; R 2 = 0.90 with multiple concurrent input features. Siar et al [31] implemented an AlexNet-based CNN architecture for brain age classification. The age groups are separated into five categories ranging from 10 to 70 years.…”

Section: Introductionmentioning

confidence: 99%

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation

Pilli,

Goel,

Murugan

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

show abstract

“…This study has illustrated a classifier which is a combination of clustering algorithms for feature extraction and CNN that achieved a final accuracy of 99.12%. We had collected the dataset of this study from image centres and also used that dataset in other work [10] to detect patients' age from brain MRI images. Since doctors are verified that detecting patients' gender is not possible from MRI images and eyes, we had investigated this problem in [10].…”

Section: Introductionmentioning

confidence: 99%

“…We had collected the dataset of this study from image centres and also used that dataset in other work [10] to detect patients' age from brain MRI images. Since doctors are verified that detecting patients' gender is not possible from MRI images and eyes, we had investigated this problem in [10]. We had used a CNN classifier to categorize the images into five age classes, which achieved an accuracy of 79%.…”

Section: Introductionmentioning

confidence: 99%

A combination of feature extraction methods and deep learning for brain tumour classification

Siar

Teshnehlab

2021

IET Image Processing

Self Cite

View full text Add to dashboard Cite

This paper presents a method for categorizing tumour disease from magnetic resonance imaging images using a convolutional neural network. The proposed technique consists of three major phases, including feature extraction, feature selection, and combination. The authors considered the classification method using convolutional neural network without any pre-processing on input images as the original method. The original method is then improved in some sequential phases when convolutional neural network uses features generated by feature extraction methods. Many popular feature extraction methods in generating the input features of the network are examined. After examining the results, a set of feature extraction schemes with appropriate performance are selected for the next phase. Also, the authors assigned weight factors to each of the selected methods, according to their accuracy. By assigning these weight factors to the methods, the network's accuracy increased to an acceptable level compared to the first and second observations. An accuracy above 99.76% was achieved, which is a 2% improvement using these feature extraction methods compared to the original method. The authors also added a third class named 'I do not know' to increase the tumour detection problem's reliability in the last phase. The authors have successfully introduced a convolutional neural network architecture with high accuracy and reliability.

show abstract