DEEP LEARNING YOU CAN TRUST
How it Works
01
Input: A raw MRI scan from a patient
02
Feature Extraction: Neuroflux extracts edges from a textural analysis of the MRI images, identifying cellular morphology and key diagnostic indicators
03
Deep Learning Model: Images and features pass through CNN layers for spatial pattern recognition to identify abnormalities, and metadata is processed for sequential analysis
04
Tumor Identification: Neuroflux classifies the tumor type and outputs its predicition
05
Explainability: The input image and features are fed through a Grad-CAM model to identify which regions most influenced Neuroflux's prediction, and the heatmap is output
06
Clinician Validation: Radiologists review Neuroflux's prediction
Our Results Speak Volumes
1
Accuracy - 99.01%
The percentage of GRAD-Cam pixels that Neuroflux's heatmaps accurately predict
2
Sensitivity - 98.85%
The percent of tumors detected by Neuroflux when they are present
3
Specificity - 99.7%
The percent of non-tumors that Neuroflux doesn't classify as tumors