Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the disease efficiently. Traditional skin cancer detection methods face scalability challenges and overfitting...
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.
BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the div...
International journal of medical informatics
Jan 30, 2025
BACKGROUND: With advancements in medical technology and science, chronic obstructive pulmonary disease (COPD), one of the world's three major chronic diseases, has seen numerous remarkable outcomes when combined with artificial intelligence, particul...
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
PURPOSE: The largest cause of cancer-related fatalities worldwide is lung cancer. The dimensions and positioning of the primary tumor, the presence of lesions, the type of lung cancer like malignant or benign, and the good mental health diagnosis all...
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...
OBJECTIVES: To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.
Journal of occupational and environmental medicine
Jan 28, 2025
OBJECTIVE: The aims of the study were to develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs and to assess its predictive capacity.
Journal of X-ray science and technology
Jan 28, 2025
ObjectiveThe goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, se...
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