Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inher...
Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subty...
Low birthweight (LBW) is a significant health challenge worldwide, as these neonates experience both short- and long-term disabilities. Factors affecting maternal and fetal health during early to mid-pregnancy can greatly influence fetal development....
The critical role of gut microbiota in human health and disease has been increasingly illustrated over the past decades, with a significant amount of research demonstrating an unmet need for self-monitor of the fecal microbial composition in an easil...
To enhance thrombolysis eligibility in acute ischemic stroke, we developed a deep learning model to estimate stroke onset within 4.5 h using diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images. Given the variabilit...
Human's facial expressions and emotions have direct impact on their action and decision-making abilities. Basic CNN models are complexity of speeding up the operation to minimize the complexity. In this paper, we have proposed a Deep Convolutional Ne...
Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, thus a global priority is the identificati...
As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse codin...
Antibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target an...
We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellu...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.