The Silent Crisis in Lung Health
Every year, respiratory diseases claim over 4 million lives globally. During the COVID-19 pandemic, we witnessed firsthand how critical early detection is for lung conditions. But with radiologists overwhelmed and diagnostic resources scarce in many regions, there’s an urgent need for accessible screening tools.
Enter X-RayGuard - our open-source deep learning solution that brings hospital-grade diagnostic capabilities to any device with a camera.
How X-RayGuard Works
Cutting-Edge Architecture
from tensorflow.keras.applications import MobileNetV2
model = tf.keras.Sequential([
MobileNetV2(input_shape=(96,96,3),
GlobalAveragePooling2D(),
Dense(64, activation='relu'),
Dropout(0.3),
Dense(3, activation='softmax') # COVID/Normal/Pneumonia
])Our transfer learning approach leverages MobileNetV2’s powerful feature extraction capabilities, fine-tuned with specialized layers for medical imaging. The model accepts 96x96 pixel X-ray images and outputs probability distributions across three critical classes.
Unprecedented Transparency
What sets LungVisionAI apart is its explainability through Grad-CAM technology:
Model highlights decision regions - critical for doctor validation
Performance That Matters
| Metric | COVID-19 | Normal | Pneumonia |
|---|---|---|---|
| Precision | 89% | 95% | 94% |
| Recall | 88% | 96% | 92% |
| F1-Score | 88% | 95% | 93% |
Overall Accuracy: 93.5% - validated on 3,031 test images from diverse sources.
GitHub Repository
Disclaimer: Always validate AI predictions with certified medical professionals. This tool assists but doesn’t replace human diagnosis.