This project was done as part of Capstone Project for PGP in Artificial Intelligence and Machine Learning by Great Learning
The project is built on Google Colab Jupyter Notebook and Kaggle.
In this capstone project, the goal is to build a Pneumonia Detection System, to locate the position of inflammation in an image. In all, the project objective can be described as:
- Build a reliable Pneumonia Detection Model which can have a robust backing.
- Proper pre-processing and meaningful Exploratory Data Analysis.
- The medical images dataset can be properly trained by a deep learning network with custom architectures
- Use transfer learning to facilitate training with final layers of the deep network trainable
- Learn to fine tune the model by trying different optimizers, loss functions, epochs, learning rate, batch size, check pointing, early stopping etc.
- Read different research papers of given domain to obtain the knowledge of advanced models for the given problem.
- Advocate a strong backing case for the reliability of the model finally obtained by proposing a use case confidence interval.
- Understanding the data with a brief on train/test labels and respective class info
- Look at the first five rows of both the csvs (train and test)
- Identify how are classes and target distributed
- Check the number of patients with 1, 2, ... bounding boxes
- Read and extract metadata from dicom files
- Perform analysis on some of the features from dicom files
- Check some random images from the training dataset
- Draw insights from the data at various stages of EDA
- Visualize some random masks generated
Outcome
- Jupyter Notebook Link containing the exploration steps.
- Split the data
- Use different models to train the data. Here we are using UNet to train our dataset with different backbone structures
- Evaluate the models (ROC AUC, AP, F1 Score)
Outcome
- Classification - Jupyter Notebook Link with the UNet architecture with pretrained ImageNet weights. Evaluating the model on average precision, accuracy and ROC AUC.