ViResNet ‐ SCD : XAI ‐Powered Vision Transformer and ResNet Integration for Sickle Cell Disease Detection in Microscopic Images

Sickle Cell Disease (SCD), the most prevalent inherited disease worldwide, requires the rapid and accurate detection of sickle‐shaped red blood cells. In Hematology, manual examination of sickle cells in blood smears is time‐consuming and prone to qualitative errors. Artificial intelligence has transformed healthcare, offering revolutionary solutions to diagnostic problems. The current experiment proposes a hybrid feature fusion framework combining ResNet‐50 and Vision Transformer (ViT) to overcome the limitations of individual convolutional or transformer‐based models, known as ViResNet‐SCD. The model integrates the local features of microscopic images from ResNet‐50 and distinguishes global spatial relationships through ViT. The model is trained on a well‐documented dataset, and the dataset has been augmented with state‐of‐the‐art data augmentation strategies to handle the class imbalance issue. The final dataset consists of 10 000 images in training (70%), validation (15%), and testing (15%). For facilitating clinical confidence and model interpretability, Gradient weighted Class Activation Mapping (Grad‐CAM) was utilized to highlight the important morphological regions that influence the model's classification. The hybrid model attained a test set accuracy of 98.80%, outperforming the baseline models, namely ResNet‐50 and ViT, across all assessment metrics. For statistical significance, the architecture also attained a Cohen's Kappa and Matthews Correlation Coefficient (MCC) value of 0.976, indicating wide agreement and balanced classification. Comparative analysis with state‐of‐the‐art methods shows a significant improvement in the results produced by the proposed method.