Deep Learning-Driven Scalable and High-Precision Malaria Detection from Microscopic Blood Smear Images
N. Kannaiya Raja, Divya Rohatgi, Venkata Lalitha Narla, Ganesh Kumar Anbazhagan, R. Aroul Canessane, Drakshayani Sriramsetti, Yousef A. Baker El–Ebiary · International Journal of Advanced Computer Science and Applications · 2025
Malaria continues to be a life-threatening disease, especially in tropical and low-resource regions, where timely and accurate diagnosis remains a major challenge. Traditional diagnostic approaches like manual microscopy are not only time-consuming and expertise-dependent but also prone to subjective errors. Existing deep learning methods, such as Convolutional Neural Networks (CNNs), ResNet, and Vision Transformers (ViT), struggle to generalize across variations in staining, resolution, and morphology, leading to misclassification and reduced diagnostic reliability. To overcome these limitations, this study proposes a novel hybrid architecture, Swin-Siamese, which integrates the hierarchical self-attention mechanism of the Swin Transformer with the contrastive similarity learning capability of the Siamese Neural Network. This unique combination enables the model to capture both global and local spatial patterns while accurately distinguishing infected from uninfected blood smear images. The model is implemented using TensorFlow and PyTorch, and trained on a publicly available malaria dataset comprising 13,152 training, 626 validation, and 1,253 test images. Experimental results demonstrate a 3.1% improvement in accuracy over traditional CNNs, achieving 95.3% accuracy, 95.1% precision, 95.4% recall, 95.2% F1-score, and an AUC-ROC of 0.97. This significant performance gain highlights the model's scalability, interpretability, and real-time applicability in clinical and field-deployable diagnostic systems, offering a powerful solution for malaria screening in underserved regions.