Interpretable Deep Learning Framework for COVID-19 Detection: Grad-CAM Integration with Pre-trained CNN Models on Chest X-Ray Images - Ammar A. Ali (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
This study is present a novel approach for interpretability enhancing of the deep learning models (EfficientNet, ResNet, VGG) that applied to COVID-19 diagnosis by using the Gradient-Weighted Class Activation Mapping (Grad-CAM) all that to make transparent decision-making improved. To do this we leveraging the capabilities of Grad-CAM, and we aim to provide not only accurate diagnostic predictions but also give a visual explanations, that support the professionals in the healthcare to understanding the underlying features that aided to the model’s decisions. This interpretability is important for building trust in the AI systems, especially in medical areas diagnosis that critical such. This interpretability is essential for building trust in the AI systems, especially in critical areas such as medical diagnosis, that is allowing healthcare professionals to understand the rationale behind the AI-generated recommendations and decisions. In the context of COVID-19, using techniques like Gradient-Weighted Class Activation Mapping (Grad-CAM) can provide insights into which features of medical imaging data contribute most significantly to the model’s predictions, this enhancing reliability and transparency of the AI system. This capability not only aids clinicians in understanding the rationale behind AI-driven diagnoses but it is also fosters greater trust in the automated systems, especially in high-stakes scenarios like healthcare. It is crucial this transparency is ensuring that healthcare professionals can make informed decisions based on the AI’s outputs. As the COVID-19 pandemic demonstrated, timely and accurate diagnosis is an essential for the effective patient management.