Discover why combining microwave imaging and acoustic analysis achieves 99.3% accuracy—far beyond either modality alone—and what this means for clinical screening.

Learn how XGBoost (eXtreme Gradient Boosting) combines microwave features and acoustic probabilities to achieve 99.3% accuracy in tumor detection.

Explore how YAMNet converts lung sounds into 1024-dimensional embeddings, capturing wheezes, crackles, and breath patterns to classify respiratory diseases.

Discover the three key electromagnetic properties that distinguish tumors from healthy tissue—permittivity, conductivity, and loss tangent—and how PULMO-AI uses them for detection.

Learn how the Inverse Fast Fourier Transform converts frequency-domain S21 data into time-domain reflections, helping PULMO-AI locate tumors by measuring signal delays.

Learn the physics and functionality behind microwave imaging by mastering these essential equations