D3: Test Plan

Ideal Scenario

Section I: Ideal Testing Scope with Unlimited Resources

Extensive Training and Model Accuracy Testing:

• Primary Objective: With unrestricted time and resources, the core focus will be on diligently training the GAN model to ensure high accuracy in segmenting brain MRI scans.
• Key Aspects:
  • Rigorous training cycles to fine-tune the model for precise segmentation of cerebral spinal fluid, brain matter, and other categories.
  • Implementing comprehensive accuracy tests to evaluate the segmentation results against benchmarks and expert-verified data.

Exploring and Testing Diverse Architectures:

• Research and Optimization:
  • Given the project’s dual nature as a research initiative and an optimization challenge, a significant portion of our efforts would be channeled into exploring and testing a wide array of model architectures.
• Testing Approaches:
  • Conducting parallel tests across different GAN architectures to determine the optimal configuration for accuracy and performance.

Section II: Practical Testing Plan

Unit Testing:

• Ensuring proper uploading and processing of NIFTI files.
• Verifying each step from preprocessing to running through the model.

Integration and System Testing:

• Testing the upload functionality on the web app.
• Verifying the processing and model output visualization on the frontend.

Tools Used:

• Command-line interface, React for frontend (for class purposes)
• Google Colab, Python, TensorFlow.

End User Types:

• Hospital staff for processing MRI scans.
• Researchers and students for segmenting and studying MRI scans.

Performance and Reliability Testing:

• Ensuring the model processes images similar but not identical to the training data.

Acceptance Testing:

• Producing reasonable segmented results with minimal unrealistic areas.
• Achieving segmentation close to manually segmented images.