Deep sequence modelling for Alzheimer's disease detection using MRI

Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis. In this study, the images were enhanced and the active contour algorithm (ACA) was used to extract regions of interest (ROI) such as soft tissue and white matter. Strategies have been developed to diagnose AD and differentiate its stages. The first strategy is using XGBoost and ANN networks with the features of MobileNet, DenseNet, and GoogLeNet models. The second strategy is by XGBoost and ANN networks with combined features of MobileNet-DenseNet121, DenseNet121-GoogLeNet and MobileNet-GoogLeNet. The third strategy combines XGBoost and ANN networks with combined features of MobileNet-DenseNet121-Handcrafted, DenseNet121-GoogLeNet-Handcrafted, and MobileNet-GoogLeNet-Handcrafted leading to improved accuracy of the strategies and improved efficiency. XGBoost with hybrid features of DenseNet-GoogLeNet-Handcrafted achieved an AUC of 98.82%, accuracy of 98.8%, sensitivity of 98.9%, accuracy of 97.08%, and specificity of 99.5%.

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism

Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. However, the multimodal data is often complex and inconsistent, which leads to inadequate feature extraction. To address the problem, We propose a model for early diagnosis of Alzheimer's Disease based on multimodal attention(EDAMM). Specifically, we first evaluate and select three optimal feature extraction methods, Wav2Vec2.0, TF-IDF and Word2Vec, to extract acoustic and linguistic features. Next, by leveraging self-attention mechanism and cross-modal attention mechanisms, we generate fused features to enhance and capture the inter-modal correlation information. Finally, we concatenate the multimodal features into a composite feature vector and employ a Neural Network(NN) classifier to diagnose Alzheimer's Disease. To evaluate EDAMM, we perform experiments on two public datasets, i.e., NCMMSC2021 and ADReSSo. The results show that EDAMM improves the performance of Alzheimer's Disease diagnosis over state-of-the-art baseline approaches on both datasets.

ERABiLNet: enhanced residual attention with bidirectional long short-term memory

Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented "Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)" is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer's detection in scale of 2-5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to "Residual Attention Network (RAN)", which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the "Attention-based Bi-LSTM". The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

A robust and clinically applicable deep learning model for early detection of Alzheimer's

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Reinforcement-Learning-Based Localization of Hippocampus for Alzheimer's Disease Detection

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily impacting memory and cognitive functions. The hippocampus serves as a key biomarker associated with AD. In this study, we present an end-to-end automated approach for AD detection by introducing a reinforcement-learning-based technique to localize the hippocampus within structural MRI images. Subsequently, this localized hippocampus serves as input for a deep convolutional neural network for AD classification. We model the agent-environment interaction using a Deep Q-Network (DQN), encompassing both a convolutional Target Net and Policy Net. Furthermore, we introduce an integrated loss function that combines cross-entropy and contrastive loss to effectively train the classifier model. Our approach leverages a single optimal slice extracted from each subject's 3D sMRI, thereby reducing computational complexity while maintaining performance comparable to volumetric data analysis methods. To evaluate the effectiveness of our proposed localization and classification framework, we compare its performance to the results achieved by supervised models directly trained on ground truth hippocampal regions as input. The proposed approach demonstrates promising performance in terms of classification accuracy, F1-score, precision, and recall. It achieves an F1-score within an error margin of 3.7% and 1.1% and an accuracy within an error margin of 6.6% and 1.6% when compared to the supervised models trained directly on ground truth masks, all while achieving the highest recall score.

Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.

End-to-end automatic pathology localization for Alzheimer's disease diagnosis using structural MRI

Structural magnetic resonance imaging (sMRI) is an essential part of the clinical assessment of patients at risk of Alzheimer dementia. One key challenge in sMRI-based computer-aided dementia diagnosis is to localize local pathological regions for discriminative feature learning. Existing solutions predominantly depend on generating saliency maps for pathology localization and handle the localization task independently of the dementia diagnosis task, leading to a complex multi-stage training pipeline that is hard to optimize with weakly-supervised sMRI-level annotations. In this work, we aim to simplify the pathology localization task and construct an end-to-end automatic localization framework (AutoLoc) for Alzheimer's disease diagnosis. To this end, we first present an efficient pathology localization paradigm that directly predicts the coordinate of the most disease-related region in each sMRI slice. Then, we approximate the non-differentiable patch-cropping operation with the bilinear interpolation technique, which eliminates the barrier to gradient backpropagation and thus enables the joint optimization of localization and diagnosis tasks. Extensive experiments on commonly used ADNI and AIBL datasets demonstrate the superiority of our method. Especially, we achieve 93.38% and 81.12% accuracy on Alzheimer's disease classification and mild cognitive impairment conversion prediction tasks, respectively. Several important brain regions, such as rostral hippocampus and globus pallidus, are identified to be highly associated with Alzheimer's disease.

An Approach to Binary Classification of Alzheimer's Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

BACKGROUND

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.

METHODS

We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.

RESULTS

Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.

CONCLUSIONS

The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives

Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the radiologists in producing accurate results toward effective disease diagnosis. The objective of this research is to highlight the importance of deep learning models in the detection of Alzheimer's Disease (AD). The main objective of this research is to analyze different deep learning methods used for detecting AD. This study examines 103 research articles published in various research databases. These articles have been selected based on specific criteria to find the most relevant findings in the field of AD detection. The review was carried out based on deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL). To propose accurate methods for the detection, segmentation, and severity grading of AD, the radiological features need to be examined in greater depth. This review attempts to analyze different deep learning methods applied for AD detection using neuroimaging modalities like Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), etc. The focus of this review is restricted to deep learning works based on radiological imaging data for AD detection. There are a few works that have utilized other biomarkers to understand the effect of AD. Also, articles published in English were alone considered for analysis. This work concludes by highlighting the key research issues towards effective AD detection. Though several methods have yielded promising results in AD detection, the progression from Mild Cognitive Impairment (MCI) to AD need to be analyzed in greater depth using DL models.

Artificial Intelligence Model for Alzheimer's Disease Detection with Convolution Neural Network for Magnetic Resonance Images

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores.

OBJECTIVE

This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition.

METHODS

In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques.

RESULTS

The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%.

CONCLUSION

This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses.

A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma

BACKGROUND AND OBJECTIVE

Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images. Therefore, developing a non-invasive method that can predict the invasiveness and boundary of CP as a reference before surgery is of clinical value for making more appropriate and individualized treatment decisions and reducing the occurrence of inappropriate surgical plan choices.

METHODS

The MT-Brain system has consisted of two pathways, a sub-path based on 2D CNN for capturing the features from each slice of MRI images, and a 3D sub-network for capturing additional context information between slices. By introducing the two-path architecture, our system can make full use of the fusion of the above 2D and 3D features for classification. Furthermore, position encoding and mask-guided attention also have been introduced to improve the segmentation and diagnosis performance. To verify the performance of the MT-Brain system, we have enrolled 1032 patients with craniopharyngioma (302 invasion and 730 non-invasion patients), segmented the tumors on postcontrast coronal T1WI and randomized them into a training dataset and a testing dataset at a ratio of 8:2.

RESULTS

The MT-Brain system achieved a remarkable performance in diagnosing the invasiveness of craniopharyngioma with the AUC of 83.84%, the accuracy of 77.94%, the sensitivity of 70.97%, and the specificity of 80.99%. In the lesion segmentation task, the predicted boundaries of lesions were similar to those labeled by radiologists with the dice of 66.36%. In addition, some explorations also have been made on the interpretability of deep learning models, illustrating the reliability of the model.

CONCLUSIONS

To the best of our knowledge, this study is the first to develop an integrated deep learning model to predict the invasiveness of craniopharyngioma preoperatively and locate the lesion boundary synchronously on MRI. The excellent performances indicate that the MT-Brain system has great potential in real-world clinical applications.

An Intelligent System for Early Recognition of Alzheimer's Disease Using Neuroimaging

Alzheimer's disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.