Language impairment is a key biomarker for neurodegenerative diseases such as Alzheimer's dis-ease (AD). With the rapid growth of Large Language Models, natural language processing (NLP) has be-come a preferred modality for the early prediction of AD from speech. In this work, we propose a two-stage process for early detection of AD from transcriptions of speech. The first step involves extracting a discriminative text embedding representation using public models from OpenAI. This embedding serves as input for a machine learning classifier in the second stage. In this paper, we investigate three text embedding models and eight machine learning classifiers, both deep learning (DL) based and non-DL based. The evaluation was conducted using the public ADReSSo dataset of 237 patients. The results show that models “ada-002” and “3-small” produce discriminative embeddings that lead to good performance when combined with a Deep Neural Network in classification, achieving accuracy rates of 83.10% and 84.51 %, respectively.

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression. This study investigates the coherence of SHAP values with medical scientific truth. It examines three types of features: clinical, demographic, and FreeSurfer extracted from MRI scans. A set of six ML classifiers are investigated for their interpretability levels. This study is validated on the OASIS-3 dataset with binary classification. The results show that clinical data outperforms the others, with a margin of 14% over FreeSurfer features, the second-best features. In the case of clinical features, the explanations provided by the tree-based classifiers consistently align with medical insights. This comparison was calculated using the Kendall Tau distance.

Predicting strokes is essential for improving healthcare outcomes and saving lives. This paper introduces a benchmarking dataset, PredictStr, specifically developed to enhance stroke prediction. This dataset improves upon a previously unique dataset identified in the literature. Our methodology comprises two main steps: firstly, we outline a series of preprocessing and cleaning measures to enhance data quality. Secondly, we present a novel algorithm, the Dynamic Hybrid Balancing Algorithm, which builds upon the ADSYSN algorithm by integrating consistency constraints to address class imbalances. Our contribution extends to the application of sophisticated analysis techniques, including histogram and boxplot analyses, feature distribution assessments, statistical explorations, correlation evaluations, feature importance rankings, and Individual Conditional Expectation (ICE) plots. These methodologies are designed to provide valuable insights into feature significance, thereby assisting researchers in identifying the most critical attributes for effective stroke detection.

Alzheimer's disease is a neurodegenerative disorder defined by memory loss and primarily affects older individuals. Currently, there is no definitive cure available. Although medications are accessible, they only serve to slow the progression of the disease. In this paper, we propose the use of Vision Transformers and Transfer Learning for Alzheimer's classification. Our approach leverages the temporal aspect of the transformer to model the correlation between different image patches. Transfer learning enables us to mitigate the issue of insufficient available data. Our method has been validated on the OASIS dataset, which consists of 250 brain scans. The results demonstrate that transfer learning with Transformer models surpasses the performance of transfer learning with CNN models by 4% and exceeds traditional CNN models without transfer learning by 8%. Two types of Transformers were tested: ViT-B16 and ViT-B32. The results are comparable, with ViT-B32 outperforming ViT-B16 by 1%.

Alzheimer’s disease (AD) is a chronic and irreversible brain disorder, making early detection crucial in managing its progression. This study aims to compare multiple deep learning models for the early detection of Alzheimer’s disease. The research employs advanced techniques, including convolutional neural networks (CNNs) and transfer learning models. Specifically, six deep neural network techniques - 2DCNN, VGG19, DenseNet121, Inception-V3, MobileNet and ResNetl0lv2 are utilized to classify and identify different stages of AD, such as NonDementia, Very Mild Dementia, Mild Dementia and Moderate Dementia. The comparative study is validated on the Kaggle dataset of 6400 MRI images. The results demonstrate that the CNN outperforms the fine-tuned architectures with an accuracy of 99.14% and AUC of 99.84%.

The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor extracts salient points associated each with a local region of one of the objects. Two reidentification systems are proposed: an IP-based system using SURF interest points matched via sparse representation, and Image-based system using a Convolutional Neural Network. To harness both representations, we propose a fusing strategy based on the scores product rule, the scores being vote vectors associated with each descriptor for each person. Our proposal is evaluated on the large public dataset PRID-2011 and the results show its effectiveness compared to the state of the art.

We present in this paper a multi-shot human reidentification system from video sequences based on SURF matching. Our contribution is about the matching step which is crucial. In this context, we propose a new method of SURF matching via sparse representation. Each SURF Interest Point in the test sequence is represented by a sparse representation of SURFs points in the reference dataset. For efficiency purposes, a dynamic dictionary is selected for each SURF from this dataset through KD-Tree Neighborhood search. Then a majority vote rule is applied to classify the test sequence. This approach is evaluated on two public datasets : PRID-2011 and CAVIAR4REID. The experimental results show that our approach compares favorably with and outperforms current state-of-the-art on the two datasets by 1% to 7%.