Search

Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3–5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability. [ABSTRACT FROM AUTHOR]

In recent years, machine learning algorithms have been widely adopted across many fields due to their efficiency and versatility. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the predictions of a pre-trained model. Drawing on these improvements, practitioners seek to gain causal insights into the underlying data-generating mechanisms. To this end, works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. In this paper, we argue that each question about a causal effect requires its own reasoning and that relying on an initial predictive model trained on an arbitrary set of variables may result in quantification problems when estimating all possible effects. As an alternative, we advocate for a query-driven methodology that addresses each causal question separately. Assuming that the causal structure relating the variables is known, we propose to employ the tools of causal inference to quantify a particular effect as a formula involving observable probabilities. We then derive conditions on the selection of variables to train a predictive model that is tailored for the causal question of interest. Finally, we identify suitable eXplainable AI (XAI) techniques to estimate causal effects from the model predictions. Furthermore, we introduce a novel method for estimating direct effects through intervention on causal mechanisms. [ABSTRACT FROM AUTHOR]