An Explainable AI for Blood Image Classification With Dynamic CNN Model Selection Framework

Abstract

Explainable AI (XAI) frameworks are becoming essential in many areas, including the medical field, as they help us to understand AI decisions, increasing clinical trust and improving patient care. This research presents a robust and comprehensive Explainable AI framework. To classify images from the BloodMNIST and Raabin-WBC datasets, various pre-trained convolutional neural network (CNN) architectures: the VGG, the ResNet, the DenseNet, the EfficientNet, the MobileNet variants, the SqueezeNet, and the Xception are implemented both individually and in combination with SpinalNet. For parameter analysis, four models, VGG16, VGG19, ResNet50, and ResNet101, were combined with SpinalNet. Notably, these SpinalNet hybrid models significantly reduced the model parameters while maintaining or even improving the model accuracy. For example, the VGG 16 + SpinalNet shows a 40.74% parameter reduction and accuracy of 98.92% (BloodMnist) and 98.32% (Raabin-WBC). Similarly, the combinations of VGG19, ResNet50, and ResNet101 with SpinalNet resulted in weight parameter reductions by 36.36%, 65.33%, and 52.13%, respectively, with improved accuracy for both datasets. These hybrid SpinalNet models are highly efficient and well-suited for resource-limited environments. The authors have developed a dynamic model selection framework. This framework optimally selects the best models based on prediction scores, prioritizing lightweight models in cases of ties. This method guarantees that for every input, the most effective model is used, which results in higher accuracy as well as better outcomes. Explainable AI (XAI) techniques: Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive ExPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM) are implemented. These help us to understand the key features that influence the model predictions. By combining these XAI methods with dynamic model selection, this research not only achieves excellent accuracy but also provides useful insights into the elements that influence model predictions.