A Framework for Integrating Deep Learning and Symbolic AI Towards an Explainable Hybrid Model for the Detection of COVID-19 Using Computerized Tomography Scans - Vengai Musanga; Serestina Viriri; Colin Chibaya (Information)
The integration of Deep Learning and Symbolic Artificial Intelligence (AI) offers a promising hybrid framework for enhancing diagnostic accuracy and explainability in critical applications such as COVID-19 detection using computerized tomography (CT) scans. This study proposes a novel hybrid AI model that leverages the strengths of both approaches: the automated feature extraction and classification capabilities of Deep Learning and the logical reasoning and interpretability of Symbolic AI. Key components of the model include the adaptive deformable module, which improves spatial feature extraction by addressing variations in lung anatomy, and the attention-based encoder, which enhances feature saliency by focusing on critical regions within CT scans. Experimental validation using performance metrics such as F1-score, accuracy, precision, and recall demonstrates the model’s significant improvement over baseline configurations, achieving near-perfect accuracy (99.16%) and F1-score (0.9916). This hybrid AI framework not only achieves state-of-the-art diagnostic performance but also ensures interpretability through its symbolic reasoning layer, facilitating its adoption in healthcare settings. The findings underscore the potential of combining advanced machine learning techniques with symbolic approaches to create robust and transparent AI systems for critical medical applications.