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An Explainable AI driven Decision Support System for COVID-19 Diagnosis using Fused Classification and Segmentation.

K Niranjan1, S Shankar Kumar1, S Vedanth1

  • 1Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India.

Procedia Computer Science
|February 6, 2023
PubMed
Summary

A novel AI system, GGECS, aids in diagnosing COVID-19 severity using chest CT scans. This explainable AI tool accurately classifies infections and identifies lesions, assisting doctors when expertise is limited.

Keywords:
COVID-19Class Activavtion MappingConvolutional Neural networksExplainable AIGradient weighted Class Activation Mapping (Grad-CAM)Guided Grad-CAMSegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is standard for COVID-19 detection but doesn't assess infection severity.
  • Chest CT scans reveal COVID-19 lesions with high sensitivity, yet expert analysis is resource-intensive.
  • A shortage of skilled radiologists during the pandemic highlighted the need for automated diagnostic support.

Purpose of the Study:

  • To develop a real-time, explainable AI system (GGECS) for classifying COVID-19 severity and identifying lesions on chest CT scans.
  • To enhance diagnostic capabilities by providing decision support for medical professionals.
  • To leverage explainable AI techniques for transparent and interpretable model predictions.

Main Methods:

  • Proposed a Guided Gradcam based Explainable Classification and Segmentation system (GGECS).
  • Employed a Res2Net-inspired classification model and integrated explainable AI (GradCam, Guided GradCam) for CNN interpretability.
  • Developed a segmentation model fusing VGG-16 and the classification network for precise localization of infected regions.

Main Results:

  • The GGECS classification model achieved an overall accuracy of 98.51%.
  • The segmentation model demonstrated a significant performance with an IoU score of 0.595.
  • Explainable AI techniques successfully highlighted regions in CT scans crucial for the model's predictions.

Conclusions:

  • GGECS provides an effective, explainable AI-driven decision support system for COVID-19 diagnosis using chest CT scans.
  • The system demonstrates high accuracy in classification and reliable lesion localization, addressing the need for expert radiological interpretation.
  • Explainable AI integration enhances trust and utility in clinical decision-making for infectious disease imaging.