Early Diagnosis of Alzheimer's Diseases and Dementia from MRI Images Using an Ensemble Deep Learning

• URL: http://arxiv.org/abs/2412.05666v1
• Date: Sat, 07 Dec 2024 14:27:41 GMT
• Title: Early Diagnosis of Alzheimer's Diseases and Dementia from MRI Images Using an Ensemble Deep Learning
• Authors: Mozhgan Naderi, Maryam Rastgarpour, Amir Reza Takhsha,
• Abstract summary: Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. In this study, we proposed two CNNs, IR-BRAINNET and Modified-DEMNET, designed to detect the early stages of AD accurately. We also introduced an ensemble model that averages their outputs to reduce variance across the CNNs and enhance AD detection.
• Score: 0.7510165488300369
• License:
• Abstract: Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAINNET and Modified-DEMNET, designed to detect the early stages of AD accurately. We also introduced an ensemble model that averages their outputs to reduce variance across the CNNs and enhance AD detection. Both CNNs are trained, and all models are evaluated using a Magnetic Resonance Imaging (MRI) dataset from the Kaggle database. The dataset includes images of four stages of dementia, with an uneven class distribution. To mitigate challenges stemming from the inherent imbalance in the dataset, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to generate additional instances for minority classes. In the NO-SMOTE scenario, despite the imbalanced distribution, the ensemble model achieved 98.28% accuracy, outperforming IR-BRAINNET (97.26%) and Modified-DEMNET (95.54%), with Wilcoxon p-values of 2.9e-3 and 5.20e-6, respectively, indicating significant improvement in correct predictions through the use of the average function. In the SMOTE scenario, the ensemble model achieved 99.92% accuracy (1.64% improvement over NO-SMOTE), IR-BRAINNET reached 99.80% (2.54% improvement), and Modified-DEMNET attained 99.72% (4.18% improvement). Based on the experimental findings, averaging the models' outputs enhanced AD diagnosis in both scenarios, while the diversity in the dataset introduced by SMOTE-generated instances significantly improved performance. Furthermore, the compact models we proposed outperformed those from previous studies, even in the presence of an imbalanced distribution.

Related papers

• Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach [0.0]
  Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes. Recent advancements in deep learning (DL) have shown promise, but many models struggle with balancing accuracy and computational efficiency. We propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-02-01T21:06:42Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection [35.46541584018842]
  Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are detected as deviations between input and reconstruction. We construct multiple reconstructions with probabilistic diffusion models. We then analyze the resulting distribution of these reconstructions using the Mahalanobis distance to identify anomalies as outliers.
  arXiv  Detail & Related papers  (2024-07-17T11:02:31Z)
• Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models [1.7995110894203483]
  We introduce a framework called Re-Diffinet for modeling the discrepancy between the outputs of a segmentation model like U-Net and the ground truth. The results show an average improvement of 0.55% in the Dice score and 16.28% in HD95 from cross-validation over 5-folds.
  arXiv  Detail & Related papers  (2024-02-12T01:03:39Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19 [1.1146119513912156]
  We propose the Bias Free Network (RBFNet) to mitigate the impact of confounders in the training data distribution. RBFNet ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID19 dataset. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adrial Network (cGAN)
  arXiv  Detail & Related papers  (2024-01-04T13:09:45Z)
• Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs [35.46541584018842]
  Unsupervised Anomaly Detection (UAD) aims to identify any anomaly as an outlier from a healthy training distribution. generative models are used to learn the reconstruction of healthy brain anatomy for a given input image. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image.
  arXiv  Detail & Related papers  (2023-12-07T11:03:42Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.