A Lightweight CNN Model for Interpretable White Blood Cell Classification with Grad-CAM and Grad-CAM++

A lightweight CNN model for efficient and interpretable white blood cell classification, achieving 84% accuracy across four WBC types. Grad-CAM and Grad-CAM++ visualizations provide class-specific attention maps aligned with morphological features, making it suitable for real-time diagnostic support.