Explainable AI for Chest Diagnosis Prediction

Significant advancement has been achieved in medical reasoning (Artificial intelligence). However, the interpretability and confidence of many deep learning model discovery ideas face difficulties in fundamental applications like COVID-19 identification. The compatibility of Explainable AI (XAI) techniques with COVID-19 results according to chest X-ray pictures is examined in this work. Our model achieves uncompromising exactness and provides interpretable experiences in its dynamic interaction by combining Local Interpretable Model Agnostic Explanations (LIME) and the VGG16 architecture in conjunction with a transfer learning technique. Upgrading transparency, confidence, and comprehension in artificial intelligence-driven clinical diagnostics is the goal of the research.