What is it about?
This paper presents a Convolutional Neural Network (CNN) framework designed to classify chest X-ray images into three categories: COVID-19, Normal, and Viral Pneumonia. To ensure reliability and model integrity, the study integrates standard sanity checks alongside Grad-CAM visualization techniques to highlight key image regions involved in predictions.
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Why is it important?
Deep neural networks can act as opaque "black boxes," creating risks of misdiagnosis in critical medical scenarios. Incorporating systematic sanity checks and explainable AI techniques guarantees accurate predictions—achieving 94% accuracy—while delivering visual transparency to support clinical decision-making.
Perspectives
As researchers, we believe that high accuracy alone is not enough for medical AI deployment; transparency and verification are equally critical. By demonstrating how basic sanity checks and Grad-CAM visualizations can be easily integrated into neural networks, we hope to encourage safer, more trustworthy AI practices in clinical settings and medical imaging.
Zhraa Jabbar
Read the Original
This page is a summary of: Using sanity checks in deep neural networks predictions, January 2025, American Institute of Physics,
DOI: 10.1063/5.0274103.
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