The fundamental issue with drug-drug interactions (DDIs) is that they cannot be ignored or overlooked since negative drug reactions and the use of medical services as a result are detrimental to patients and increase healthcare expenses. Conventional...
Subdural hemorrhage (SDH) is a critical condition requiring prompt assessment of its progression using computed tomography (CT). This study aimed to develop a deep-learning model to predict temporal changes in SDH by leveraging Hounsfield Units (HU) ...
Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework th...
Early skin disease detection significantly improves patient survival rates, yet limited access to dermatological expertise creates an urgent need for automated diagnostic systems. In this paper, we develop a dual-branch deep learning framework that s...
With the rapid development of music streaming platforms, accurate understanding of lyric emotions has become crucial for enhancing personalized services in music recommendation systems. However, existing methods show significant limitations in proces...
Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by pres...
In some Asian countries, waxberries are special fruit that demand substantial labour for harvesting each season. To ease this burden, automated fruit-picking equipment has seen extensive development over the past decade. However, accurately segmentin...
Lung structures are critical for gas exchange and contribute to the pathogenesis of respiratory diseases, exhibiting notable lobe-specific heterogeneity. To investigate their genetic basis, we apply a deep-learning AI system and Pyradiomics to define...
OBJECTIVE: To assess the capability of a convolutional neural network trained by transfer learning on anterior segment optical coherence tomography (AS-OCT) images, Placido-disk corneal topography images and external photographs to predict age and bi...
PURPOSE: The study aimed to develop and validate a predictive model for preoperative APF using computed tomography (CT) radiomics combined with deep learning, and validating the performance of the model in an independent cohort.
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