Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …

Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US
image segmentation is crucial in image analysis. Recently, deep learning-based methods …

Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for
understanding the density and texture, allowing for the diagnosis of different medical …

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …

GeoCrack is the first large-scale open source annotated dataset of fracture traces from
geological outcrops, enabling deep learning-based fracture segmentation, setting a new …

Background To design a pulmonary ground-glass nodules (GGN) classification method
based on computed tomography (CT) radiomics and machine learning for prediction of …

Deep convolutional neural networks (CNNs) have facilitated remarkable success in
recognizing various food items and agricultural stress. A decent performance boost has …

Background Prostate cancer is one of the most common malignant tumors in middle-aged
and elderly men and carries significant prognostic implications, and recent studies suggest …

Interactive technologies that shape the traditional human-food experiences are being
explored under the emerging field of Human-Food Interaction (HFI). A key challenge in …