Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images.

INTRODUCTION

Rapid and precise malaria diagnosis is critical in resource-constrained settings to enable timely treatment and reduce mortality. Existing convolutional neural network (CNN) and capsule network hybrids, although effective, often suffer from high computational demands and limited generalizability across datasets.

METHODS

We propose Hybrid Capsule Network (Hybrid CapNet), a lightweight architecture combining CNN-based feature extraction with dynamic capsule routing for accurate parasite identification and life-cycle stage classification. A novel composite loss function-integrating margin, focal, reconstruction, and regression losses-was employed to enhance classification accuracy, spatial localization, and robustness to class imbalance and annotation noise. The model was evaluated on four benchmark malaria datasets (MP-IDB, MP-IDB2, IML-Malaria, MD-2019) and assessed for both intra- and cross-dataset performance.

RESULTS

Hybrid CapNet achieves superior accuracy with significantly reduced computational cost (1.35M parameters, 0.26 GFLOPs), rendering it suitable for mobile diagnostic applications. Experimental results demonstrate up to 100% accuracy in multiclass classification and consistent improvements over baseline CNN architectures in cross-dataset evaluations. Grad-CAM visualizations confirm that the model focuses on biologically relevant parasite regions, validating interpretability.

DISCUSSION

The proposed framework delivers a pragmatic and interpretable solution for malaria diagnosis, balancing high accuracy with minimal computational requirements, and demonstrates strong potential for deployment in real-world, resource-limited clinical environments.