- We created a conditional adversarial network model that identifies, analyzes, and isolates brain lesions from MRI scans
- Brain lesions simply mean any abnormal brain tissues that appear as bright spots on the brain in MRI scans
- The goal of this project is to increase doctors' diagnosis accuracies relating to identifying unhealthy brain tissues
- This increases the likelihood of successfully identifying and treating brain abnormalities while preventing unnecessary brain surgeries if the tissue was actually healthy
- Obtain MRI scans of brain scans and save them in a folder named 'MRIscans'
- See 'MRIscans' directory for MRI image examples
- Manually isolate the brain lesions from the images and save them in a folder named 'lesions'
- See 'lesions' directory for isolated brain lesion examples
- Run ./train.sh to train the custom model
- Run ./test.sh to test the custom model
- Open processed_test/index.html in your default browser to view the brain scan, model's guess, actual isolated lesion, and the accuracy measured against the SSIM index between the guess and the true lesion
- Utilized Materialize to design the displayed HTML results
- Materialize is a design language created by Google for constructing elegant user experiences
- Modified the code of pix2pix, a tensorflow model for images
- Altered the splitting, training, and testing portion of the code to tailor the model to learn brain lesion segmentations
- Fine-tuned the parameters of the model to suit our custom MRI scans
- Model outputs a file in processed/index.html which displays the final test results
- Results display brain scan, model's guess of where the lesion is, and the actual isolated lesion
- Incorporated pyssim to test the Structural Similarity Image Metric (SSIM) between the model's guess and the ground truth
- ./test.sh automatically computes the accuracy between all guesses and actual lesions
- Once computed, the script updates processed_test/index.html with the corresponding accuracies