When we first got started in Deep Learning particularly in Computer Vision, our goal is to detect all instances of objects of a known category in an image . Now, with the successful object detection workflow with the deep learning network trained models for object localisation , you can solve your problems, specifically in medical image tracking and disease diagnosis, face recognition ,traffic signs and vehicle tracking.

Figure shows the workflow and final results of an object tracking system trained with PyFasterRCNN algorithm.

Our team has experience in research and development of multiple artificial intelligence systems that can detect anomalies in X-ray or DICOM images.

Our solutions focus on making use of latest Artificial Intelligence/Machine Learning image analytics to get great accuracy and speed such us less than ten milliseconds.This helps in providing accurate better results, than conventional manual analysis, in different scenarios where medical imaging plays a major role in diagnosis.These systems designed and developed by our team provided more insights and interpretations that usually missed in conventional and manual analysis.

OUR CASE STUDY Early Diagnosis and Prediction of Pulmonary Fibrosis