A Deap Learning based method that aligns the Myocardial T1-weighted images of CRM (Cardiac Magnetic Resonance)
Neural Neworks
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Training a YOLOv4 network for drone detection and implementation on an NVIDIA Jetson TX2 installed on a UAV -
Build video dataset of flying drones for drone detection network -
Eye super-resolution from synthetic data for improved gaze estimation in unconstrained settings -
Building models to improve transfer learning techniques for breast cancer detection using small data sets of mammograms and US scans -
Segmentation of corals using data augmentation and training of existing ConvNet -
Collect data base and train a CNN to detect drones in images -
Create data set and train Resnet for smoke classification in images -
Recognition and identification of obstacles around USV using YOLO network -
Improve the STGV algorithm for depth recovery using state of the art Deep Learning based segmentation techniques -
Identify nerve structure in ultrasound images using Deep Learning. -
Using CNN neural network for classification of human \ non human objects in thermal images -
The purpose of this project is to identify and classify Event Related Potential using competitive Neural Net architecture.Categories: Neural Neworks