The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was evaluated on a SARS-CoV-2 CT scan dataset, demonstrating superior performance across all metrics compared to baseline models. The model achieved an accuracy of 95.98%, precision of 96.07%, recall of 96.01%, F1-score of 95.98%, and AUC of 98.69%, outperforming existing approaches. These results indicate that ViTGNN is an effective diagnostic tool that can be applied beyond COVID-19 detection to other medical imaging tasks.

Thyroid cancer is a growing global health concern, necessitating reliable and accurate diagnostic tools to support early detection and clinical decision-making. This study aims to develop and implement classical and ensemble machine learning models based on clinical, demographic, and biochemical data to predict thyroid cancer risk. Pearson correlation analysis was employed to identify and select the most relevant features for model training. A range of classifiers was optimized using hyperparameter tuning and cross-validation strategies. To assess robustness and generalizability, model performance was evaluated using accuracy, precision, recall, and F1-score across two independent datasets. Results show that ensemble models, particularly CatBoost, Bagging (Random Forest), and XGBoost, achieved the highest performance, with accuracies of up to 98.70% and F1-scores of 0.99 on Dataset 2, while maintaining consistent performance on Dataset 1 with accuracies around 82.51%. Classical models such as Logistic Regression, LDA, and SVM also performed competitively, achieving up to 97.40% accuracy on Dataset 2 and 82.51% on Dataset 1. These findings demonstrate the effectiveness of combining feature selection with optimized machine learning models and highlight the potential of ensemble approaches for improving thyroid cancer risk as- sessment in clinical practice.

This study introduces a federated learning-based approach to predict HER2 status from hematoxylin and eosin (HE)-stained whole slide images (WSIs), reducing costs and speeding up treatment decisions. To address label imbalance and feature representation challenges in multisite datasets, a point transformer is proposed, incorporating dynamic label distribution, an auxiliary classifier, and farthest cosine sampling. Extensive experiments demonstrate state-of-the-art performance across four sites (2687 WSIs) and strong generalization to two unseen sites (229 WSIs).

The increasing availability of social media data has created new opportunities for using artificial intelligence (AI) to study and support mental health awareness. This paper presents an offline machine learning framework that leverages natural language processing (NLP) to classify Reddit posts into distinct mental health categories based on linguistic patterns. The dataset was collected using the PRAW API from 26 subreddits on mental health, addiction, and control topics, yielding approximately 80,000 anonymized text samples. A pre-trained DistilBERT-base-uncased transformer model was fine-tuned for multi-class classification across eleven diagnostic categories, including anxiety, depression, stress, and addiction disorders. Texts were preprocessed through normalization, keyword filtering, and category mapping. Model training employed a weighted cross-entropy loss to mitigate class imbalance, with optimization performed using the AdamW algorithm in a fully offline Jupyter Notebook environment. The model achieved an overall accuracy of 77%, a macro-averaged F1 Score of 0.736, and a weighted F1 Score of 0.772. These results indicate that transformer-based NLP models can reliably distinguish between multiple forms of mental health discourse, demonstrating their potential as supportive tools for early detection or population-level analysis. The study underscores the importance of ethical data handling, transparency, and responsible AI design when applying language models to sensitive domains such as mental health.