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[Completed] SDS CP #21 - Medical X-Ray Imaging: Pneumonia Detection

Overview:

This project involves building a convolutional neural network (CNN) to classify medical X-ray images and detect pneumonia. Targeted at beginner to intermediate-level data scientists, the project will focus on leveraging deep learning techniques to develop a robust classification model. The final model will be deployed using Streamlit, providing a user-friendly interface for real-time predictions.

Objectives:

Dataset Acquisition and Preprocessing:

  • Use the publicly available dataset of X-ray images for model training.

  • Perform data preprocessing, including resizing, normalization, and augmentation, to prepare the images for training.

Model Development:

  • Build a convolutional neural network (CNN) using deep learning frameworks such as TensorFlow or PyTorch.

  • Train and evaluate the model to classify X-ray images as normal or pneumonia.

Model Deployment:

  • Develop a Streamlit application to allow users to upload X-ray images and receive a prediction.

  • Include visualization of prediction confidence and model explanation (e.g., Grad-CAM).

Scope of Works

Phase 1: Setup (1 Week)

  • Setup GitHub repo and project folders.

  • Setup virtual environment and respective libraries.

Phase 2: Dataset Acquisition and Preprocessing (1 Week)

  • Download the chest X-ray dataset from a trusted source (e.g., Kaggle).

  • Explore and preprocess the dataset:

    • Resize images to a uniform size.

    • Normalize pixel values for faster model convergence.

    • Perform data augmentation to improve model generalization.

Phase 3: Model Development (1 Week)

  • Design a CNN architecture tailored for image classification.

  • Train the model on the dataset with proper validation.

  • Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.

  • Fine-tune the model for optimal performance.

Phase 4: Model Deployment (1 Week)

  • Build a Streamlit app to:

    • Allow users to upload X-ray images.

    • Display the model's predictions (Normal or Pneumonia).

    • Provide additional insights using Grad-CAM visualizations for explainability.


Link to GitHub: https://github.com/SuperDataScience-Community-Projects/SDS-CP021-pneumonia-detection

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