… deep learning methods and addressed the above limitations in previously published DR
detection algorithms. Deep learning … study, we adapted scalable deep learning methods to the …

… that proposed deep learning models for an automated diagnosis … studies, we identify the best
performing deep learning model … the studies on deep learning in the automated detection of …

… The purpose of this study was to test whether deep learning … an automated, DNN-based
species identification system for … a foundation for many automated identification capabilities in …

… 14 and deep learning. Our objective in this study was to evaluate different machine learning
and deep learning algorithms to classify a given video segment, ie, to identify the phase …

… Citrus disease identification is vital to ensure the quality and quantity of production and …
This study proposed a deep learning-based algorithm to perform automated identification on five …

… Approaches for automated identification of plus disease in ROP depend on traditional
approaches, such as machine learning with handcrafted features [17]. Gelman established a …

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large
areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a …

… improvements in the accuracy of automated cephalometric landmark identification. In fact,
recent approaches based on deep learning algorithms showed accuracy comparable with an …

… of the most analytically powerful tools in deep learning architecture for the image classification
… a deep learning framework based on the CNN method for fish species identification. The …

… This study used deep learning to identify orthopedic implants, with model performance
exceeding that of senior orthopedic specialists. Our best-performing network classified models …