Using Deep Learning to identify medical conditions related to Thorax Region from Radiographic X-Ray Images

Automated analysis of Chest X-ray images to diagnose various pathologies will help in overcoming the costly, time consuming and prone to error from manual analysis of them, especially using deep learning based approaches. One of such recent efforts in this direction is Classification of Common Thorax which combines the advantages of CNN based feature extraction and problem transformation methods in multi-label classification task.

So this is one of the key areas where deep learning based solution has already made an impact and has the potential to come up with even a better and well improved performance.

For this session, I am going to discuss about the problem at hand, the data-set, several approaches that has been explored and that worked quite well so far in this research. Also I am going to mention about the potential use case and the real world impact of such a real world healthcare application that can save millions of lives by early and effective detection.

Also I am going to mention about some of the key challenges faced during this research and how it can be scaled to build an end to end software solution!


Outline/Structure of the Talk

  • Introduction about the problem - 2 mins
  • About the Data - 2 mins
  • Exploratory Data Analysis - 3 mins
  • Deep Learning Models Used and Architectural Flow Diagram - 4 mins
  • Approaches and Methodology - 3 mins
  • Evaluation Metrics - 3 mins
  • Results and Conclusion - 2 mins
  • Improvements and Future Works - 1min

Learning Outcome

  • Practical use of Deep Learning in Healthcare
  • Dealing with imbalance dataset for image classification using Deep Neural Networks

Target Audience

AI Researchers, ML Engineers, DL Engineers, Data Scientists

Prerequisites for Attendees

1. Working knowledge on Machine Learning

2. Working knowledge on Deep Learning

3. Basics of maths and statistics



schedule Submitted 1 year ago

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