Deep Learning Based Model for Alzheimer's Disease Detection Using Brain MRI Images

Mamun, Muntasir; Shawkat, Siam Bin; Ahammed, Md Salim; Uddin, Md. Milon; Mahmud, Md Ishtyaq; Islam, Asm Mohaimenul

doi:10.1109/uemcon54665.2022.9965730

Cited by 21 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such patterns of utilizing CNN-based transfer learning for both binary and multiclass classifications of AD were also noticed in works by Ghaffari et al [14], where ResNet101, Xception, and InceptionV3 were employed. Substantial performance metrics were reported by Mamun et al [15], where CNN outperformed other models when applied to a dataset of 6219 MRI images for AD detection. Beyond MRI data, EEG recordings have also been harnessed.…”

Section: Related Workmentioning

confidence: 82%

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

Reza,

Afrin,

Ullah

et al. 2023

IDA

View full text Add to dashboard Cite

Alzheimer's disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this affliction subtly commences within brain cells and gradually aggravates over time. In 2023, dementia's financial burden for elderly adults aged 65 and older was projected to reach $345 billion, encompassing health care, long-term care, and hospice services. Alarmingly, Alzheimer's disease claims one in three seniors, outnumbering combined fatalities from breast and prostate cancer. Currently, the diagnostic landscape for Alzheimer's lacks definitive tests, and diagnoses based purely on biological definitions have been observed to possess low predictive accuracy. In the presented study, a diagnostic methodology has been proposed using machine learning models that harness image features derived from brain MRI scans. Specifically, nine salient image features, grounded in color, texture, shape, and orientation, were extracted for the study. Four classifiers -Naïve-Bayes, Logistic regression, XGBoost, and AdaBoost -were employed, as the challenge presented a binary classification scenario. A grid search parameter optimization technique was employed to fine-tune model configurations, ensuring optimal predictive outcomes. Conducted experiments utilizing the Kaggle dataset, and for each model, parameters were rigorously optimized. The XGBoost classifier demonstrated superior performance, achieving a test accuracy of 92%, while Naïve Bayes, Logistic Regression, and AdaBoost registered accuracies of 63%, 70%, and 72%, respectively. Relative to contemporary methods, the proposed diagnostic approach exhibits commendable accuracy in predicting AD. If AI-based predictive diagnostics for AD are realized using the strategies delineated in this study, significant benefits may be anticipated for healthcare practitioners.

show abstract

Section: Related Workmentioning

confidence: 82%

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

Reza,

Afrin,

Ullah

et al. 2023

IDA

View full text Add to dashboard Cite

show abstract

“…In a study conducted by Mamun et al (2022) , 6,219 MRI images with four classes were used. The dataset was first preprocessed by performing image resizing, noise removal, image segmentation, and smoothing.…”

Section: Resultsmentioning

confidence: 99%

“…The one- versus -one and one- versus -all classification accuracies were higher than the results obtained in this study. Another difference between our study and the study by Mamun et al (2022) is that we used more MRI images with three classes, and the dataset was divided into 70% for training and 30% for testing when running the models. In addition, the EfficientNetB0, DenseNet121, and AlexNet models were employed to perform the one- versus -one and one- versus -all classifications.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test

Şener,

Acici,

Sümer

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Early diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer’s disease at a mild stage and being able to grade the disease correctly. This study’s dataset consisting of MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning-based approaches were used to obtain results. The dataset consists of three classes: Alzheimer’s disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer’s disease detection of Alzheimer’s disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer’s disease. In addition, a variant of the non-parametric statistical McNemar’s Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.

show abstract

“…Accurate diagnosis by human visual examination alone is difficult due to the wide range of practitioners’ expertise and the inherent complexity of brain tumor images [ 18 ]. MRI scanning is commonly utilized in neurology because it allows for an in-depth examination of the skull and brain [ 19 ]. It provides axial, coronal, and sagittal imaging for a more thorough evaluation [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Abdusalomov,

Mukhiddinov,

Whangbo

2023

Cancers

View full text Add to dashboard Cite

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model’s sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network’s core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

show abstract

Deep Learning Based Model for Alzheimer's Disease Detection Using Brain MRI Images

Cited by 21 publications

References 21 publications

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Contact Info

Product

Resources

About