Brain MRI Intelligence
AI-assisted classification and explainable imaging workspace for neuro-oncology research.
Clinical Benchmark Studies (5 Authentic MRI Presets)
Click any scan to immediately evaluate tumor morphology with Grad-CAM, or drag it into the dropzone:
Recent Study Activity
Live session inference log & historical MRI telemetry
| Study ID | Classified Category | Confidence | Latency | Actions |
|---|
Have your own MRI scan?
Upload custom axial, sagittal, or coronal slices (JPG / PNG) to generate interactive Grad-CAM heatmap explainability.
Analyze New MRI Scan
Upload your own brain MRI file (JPG/PNG), or select from the 5 clinical sample scans below for instant drag-and-drop evaluation.
Drop your brain MRI image here
Drag and drop a file from your computer, or drag any sample scan from below
scan-slice-axial-01.png
Ready for Analysis5 Pre-Loaded Brain Tumor MRI Samples (No file needed)
Click any scan to analyze instantly, or drag it into the dropzone above
01. Glioma Case #204
Axial T1 · Frontal Lobe02. Meningioma #118
Extra-Axial Dural Mass03. Pituitary #092
Sellar Region Adenoma04. Normal Brain #044
Healthy Control Slice05. Astrocytoma #315
Temporal HyperintensityDrag & Drop or Upload
Upload a custom JPG/PNG slice from your system or drag any of the 5 pre-loaded clinical scans directly into the dropzone.
Pre-Processing Pipeline
Input images are automatically normalized using PyTorch ImageNet mean & std distribution parameters.
Grad-CAM Explainability
The system visualizes salient convolutional activation maps (layer features[9]) in real time.
No MRI Slice Loaded
Select a sample case below or upload a new scan to begin analysis
3D Tumor Segmentation Lab
● 3D U-NET READY BraTS Protocol • 4-ChannelMulti-parametric volumetric NIfTI segmentation, BraTS subregion deconstruction (WT/TC/ET), and interactive 3D isosurface modeling.
Authentic Research Study Presets (BraTS Protocol)
Click any volumetric case to immediately run 3D sliding-window segmentation and extract 3D surface geometry:
Interactive 3D Tumor Isosurface
Derived from true segmented voxels • Marching CubesVolumetric Morphometrics
Physical integration: 1.0mm³ / voxel| SUBREGION | VOXELS | VOL (cm³) | VOL (mm³) |
|---|---|---|---|
| Whole Tumor (WT) | -- | -- | -- |
Model Explainability
Visual representation of salient feature maps and convolutional activation zones contributing to the model prediction.
01. Original MRI
Axial T1/T2 Input Slice (224×224)02. Grad-CAM Heatmap
Layer: model.features[9] (256-ch)03. Fused Overlay
Blended Anatomical & AI FocusInterpretation Protocol & Neural Feature Localization
Gradient-weighted Class Activation Mapping computes the gradients of the predicted class score with respect to the final convolutional feature maps (256 filters × 14 × 14 resolution). High activation values denote anatomical regions that most heavily influenced the neural network's classification.
Verify that peak activation maps correspond to expected intra-cranial anatomical abnormalities (e.g., hyper-intense extra-axial mass for meningioma or sellar region for pituitary). If activation occurs in non-brain background, the prediction may indicate confounding artifact.
Model Laboratory & Architecture
Detailed neural network telemetry, verified training benchmarks, and computational specifications.
Active Model Specifications
v1.0 · PyTorchVerified Training Benchmarks
EvaluatedNeural Computation Pipeline
Study History & Log
Chronological record of classified brain MRI cases in current research session.
| Study ID | Date / Time | Preview | Model Prediction | Confidence | Model | Status | Action |
|---|
No MRI studies recorded yet.
Research Reports & Print Export
Formatted academic report document for peer review, case archival, and validation logging.
| Class Category | Probability | Bar Graph |
|---|
Documentation & Research Ethics
Operating principles, data provenance, ethical boundaries, and API integration guidelines.
Ethical Boundaries & Non-Diagnostic Disclosure
NeuroScan AI is engineered exclusively as a research testbed and educational demonstration of Convolutional Neural Networks (CNNs) applied to axial brain magnetic resonance images. The system cannot replace human radiological expertise.
Dataset & Preprocessing Protocol
The neural network was trained on 5,600 verified brain MRI slices across four balanced categories: Glioma, Meningioma, No Tumor, and Pituitary. Testing was performed on a held-out set of 1,600 images achieving 94.56% validation accuracy.
- Input images are resized to 224×224 pixels with bilinear interpolation.
- Channels are normalized: Mean
[0.485, 0.456, 0.406], Std[0.229, 0.224, 0.225]. - Feature maps are extracted via four sequential Conv2D + ReLU + MaxPool blocks.
Research Methodology & Interpretability
Grad-CAM (Gradient-weighted Class Activation Mapping) computes the gradients of the target classification score with respect to the final convolutional feature map, generating coarse 2D heatmaps highlighting spatial regions influencing the model's prediction.
- Heatmap values are normalized between 0.0 (baseline) and 1.0 (peak attention).
- Red/Warm tones indicate morphological features strongly correlated with the predicted tumor class.
- Blue/Cool tones indicate lower neural weight contribution.
Technical Benchmark & Operational Telemetry
Baseline execution benchmarks and operational metrics recorded during neural evaluation:
- Model Baseline Accuracy:
94.56%(Evaluated across 1,600 verified test slices). - Inference Latency:
~35 ms(Real-time neural forward-pass latency on GPU/CPU). - Session History Tracking:
0active studies logged in local session telemetry.