NeuroScope AI is a deep learning medical assistant built with TensorFlow and Keras. It takes a brain MRI image, segments and masks suspicious tumor regions, classifies the scan as glioma, pituitary, meningioma, or no_tumor, and generates a summary of findings to support radiologists during diagnosis.
Output
4-Class Prediction
Overlay
Tumor Segmentation Mask
Summary
Findings For Review
Live Preview
Model Tasks
Segment tumor regions, classify the scan, and generate a concise findings summary from one submission.
Clinical Role
A medical assistant intended to help radiologists inspect MRI findings with faster visual and categorical cues.
Project Description
The project is centered on a deep learning neural network workflow for brain MRI interpretation. Its core model behavior is to detect whether a tumor-like region is present, segment that region, apply a mask to highlight it, and then classify the case into glioma, pituitary, meningioma, or no_tumor. The system is built to turn raw scan input into structured visual and categorical output that can be reviewed quickly.
Full Description
NeuroScope AI is built with TensorFlow and Keras and uses a U-Net based segmentation approach as part of its analysis pipeline. After processing an uploaded brain MRI, it produces a tumor mask, a multiclass prediction, and a summary of findings intended to support diagnostic review. The interface brings these outputs together in a single medical assistant workflow that helps radiologists inspect suspicious regions with more speed and structure.
Start the workflow by moving into the analysis page and submitting a brain MRI image for model processing.
The model segments the tumor region if one is detected, masks the area, and classifies the scan into one of four diagnostic categories.
Inspect the generated mask, predicted tumor class, confidence score, and summary of findings to support radiology review.