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
Accuracy - 99%
Percentage of pixels that Neuroflux's heatmaps
accurately predict
Sensitivity - 98.73%
Percentage of tumors detected by Neuroflux
when they are present
Specificity - 99.73%
Percentage of non-tumors that Neuroflux doesn't
classify as tumors