Working with Chest X-Ray (CXR) images : Medical Imaging
Neural networks have revolutionised image processing in several different domains. Among these is the field of medical imaging. In the following notebook, we will get some hands-on experience in working with Chest X-Ray (CXR) images.
The objective of this exercise is to identify images where an "effusion" is present. This is a classification problem, where we will be dealing with two classes - 'effusion' and 'nofinding'. Here, the latter represents a "normal" X-ray image.
This same methodology can be used to spot various other illnesses that can be detected via a chest x-ray. For the scope of this demonstration, we will specifically deal with "effusion".
Due to the github file-size instruction the training XRay data is not loaded here.
Please note that you need to do below to use the code:
- Create sub-directory "models" to store the best model created in present directory.
- Create two directory namely "effusion/" and "nofinding/" into one common folder "/CXR_data/" to store the input Xray images data.