ZOIDBERG 2.0

Computer Aided Diagnosis

1. Setup and Data Preparation

   • Install necessary libraries
   • A Jupyter Notebook for the project

2. Data Loading and Exploration

   • Data Loading:
     • Load the datasets using installed libraries (ex: opencv-python)
     • Display the structure and some sample to understand the data format
   • Data Exploration:
     • Visualize some X-Ray images
     • Check the distribution of classes (pneumonia vs no-pneumonia)
     • Analyze basic statistics (mean, median, standard deviation) of image pixel values

3. Data Preprocessing

   • Image Preprocessing:

     • Resize image to a standard size (ex: 224 x 224px)
     • Normalize pixel values to the range [0,1]
     • Perform data augmentation (rotation, scaling, flipping) to increase the diversity of the training data

   • Label encoding:

     • Encode the labels into numerical values if necessary (ex: no-pneumonia = 0; pneumonia = 1)

4. Splitting the Data

   • Train-Validation-Test split:

5. Model Selection and Training

6. Hyperparameter Tuning

7. Model Evaluation

8. Feature Engineering and Dimensionality Reduction

9. Final Model Training and Testing

10. Visualization and Interpretation

11. Documentation and Delivery