This is a Chest X-ray Catheter tube position classifier. When given a X-ray image, it predicts whether chest tubes are correctly positioned. To be more specific, it provides probabilistic classification of chest tube positions using Machine Learning.
ETT - Normal, ETT - Borderline, ETT - Abnormal, NGT - Abnormal NGT - Borderline, NGT - Incompletely Image, NGT - Normal CVC - Abnormal, CVC - Borderlin, CVC - Normal, Swan Ganz Catheter Present
The app is built using Streamlit! A python tool to easily Deploy ML Models.
The functioning is as follows:
_Upload an X-ray image from local system by clicking "Broswer files".
_Read the basic information from "ABOUT".
_Make the predictions using "PREDICT" and we can see the classification results (Confidence Scores for each class).When you click the "PREIDICT", it will run the predictions on 3 different models on the backend, for each model, it will return the probability of being True for all the 11 labels. The labe with highest probability will be displayed on the top.
Notice the probabilities of 11 labels don't add up to 1. Because each label is predicted independently as 0 or 1.
The models are trained with CNNs using transfer learning.
The data comes from Kaggle: https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification
Total dataset of 40,000 images, 12.23GB
Here you can only find the codes.The models have not been uploaded.