PHASE 01
Multi-Class Tensor Classification
Bypasses standard binary categorization to instantly separate clear lung fields from acute bacterial or diffuse viral infiltrates.
Thoracic Neural Inference · Live
Precision Screening
for Pulmonary
Infections.
An advanced, AI-powered thoracic evaluation system designed to instantly classify chest X-rays into Normal, Bacterial, and Viral pneumonia vectors — with deep-feature visual heatmaps.
- 3-Class
- Tensor output
- Grad-CAM
- Heatmap layer
- Auto report
● Live Inference
Classification
Bacterial · Confidence 96.3%
Grad-CAM
Active
The Core Technology Matrix
How the neural pipeline works.
Three tightly coupled inference stages — from raw radiograph ingestion to signed clinical output.
PHASE 02
Grad-CAM Focus Activation Map
Generates real-time visual heatmaps to explicitly highlight pixel regions of high diagnostic interest for clinical verification.
PHASE 03
Automated Prognosis Engine
Cross-references statistical outputs to instantly generate standardized, downloadable clinical evaluation reports.
Clinical Comparison Guide
What the model actually sees.
A quick reference to the visual signatures our deep-learning weights are trained to detect across the thoracic field.
Bacterial Matrix
Dense lobar consolidation
Looks for dense, localized lobar consolidations where infection completely opacifies specific sections of the lung fields.
Viral Matrix
Diffuse interstitial pattern
Evaluates diffuse, widespread patchy interstitial shadows or ground-glass opacities scattered symmetrically across both lungs.
Normal Matrix
Translucent lung fields
Verifies fully translucent lung fields, clear costophrenic angles, and healthy thoracic cavity positioning.
Ready to evaluate the
neural pipeline?
Access the live production terminal to test sample datasets, view Grad-CAM focus metrics, and generate instant clinical analytics sheets.
Open Deep-Learning Terminal⚠️ Legal & Clinical Demonstration Disclaimer
This system is an AI-assisted screening prototype developed exclusively for evaluation and hackathon demonstration purposes. The multi-class predictive scores, automated threshold alterations, and Grad-CAM focus metrics generated by this console are intended to support investigative clinical triage workflows and do not constitute a definitive medical diagnosis. All diagnostic outputs must be strictly reviewed, cross-referenced, and authenticated by a licensed radiologist or certified healthcare practitioner prior to any clinical intervention.