INTRODUCTION: The substantial case detection gap in the field of child tuberculosis (TB) disease is largely driven by inadequate diagnostic tools and approaches. Chest radiographs (CXRs) remain a key component in the evaluation of children and young ...
Biomedical physics & engineering express
Jan 6, 2026
. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO)...
BACKGROUND: Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains challenging in some cases.
Biomedical physics & engineering express
Dec 18, 2025
Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is critical for reducing lung cancer mortality. While convolutional neural networks (CNNs) and Transformer-based architectures have been widely used for this task, th...
Biomedical physics & engineering express
Dec 3, 2025
Lung cancer remains one of the deadliest forms of cancer worldwide, making early and accurate pulmonary-nodule classification essential for improving patient prognosis. This study presents a robust ensemble-stacking framework that integrates Histogra...
Recent advancements in deep learning have led to significant improvements in pneumoconiosis diagnosis from chest X-rays (CXR). However, these models typically require large training datasets, which are challenging to collect due to the rarity of the ...
OBJECTIVE: To evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD).
Pneumonia remains a significant global health threat, especially among children, the elderly, and immunocompromised individuals. Chest X-ray (CXR) imaging is commonly used for diagnosis, but manual interpretation is prone to errors and variability. T...
BMC medical informatics and decision making
Oct 30, 2025
OBJECTIVES: To investigate the feasibility of a deep learning model, using a transfer learning approach, for recognizing high-altitude pulmonary edema (HAPE) on chest X-ray images and exploring its capability for assessing severity.
Chest X-ray (CXR) represents one of the most widely utilized clinical diagnostic tools for thoracic diseases. Nevertheless, computer-aided diagnosis based on chest radiographs still faces considerable challenges in anomaly detection. Certain lesions ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.