Artificial intelligence, and specifically deep learning, has begun to be used at the forefront of research across a variety of domains, including healthcare. One of the areas in healthcare where deep learning can have a great impact is learning from past data to diagnose disease. Biophotonics and radiology in particular provide a large dataset of archived images that can be used in computer-aided deep learning systems to classify and diagnose health issues in patients. Deep learning, computer vision, and big data tools make it possible to automate much of the manual inspection and complex workflow in radiology and biophotonics.
Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis, exploring examples of how to automatically detect anomalies from samples of digital images using a combination of deep learning techniques, OpenCV, and Apache Spark.
Nishant Sahay is a senior architect in the Open Source COE lab at Wipro, where he is responsible for research and solution development in the area of machine learning and deep learning. Nishant has extensive experience in data analysis, design, and visualization. He has written articles on technology in online forums and presented at multiple open source conferences, such as OSI Days, GIDS, and CNCF-Kubeconf.
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