MalNet-DAF: Dual-Attentive Fusion Deep Learning Model for Malaria Parasite Classification

Malaria remains a life-threatening disease caused by Plasmodium parasites, necessitating accurate and rapid diagnosis for effective treatment. Conventional diagnostic methods are often prone to human error and limited by data insufficiency, impacting their reliability. This paper introduces MalNet-DAF (Malaria Network with Dual-Attentive Fusion), a novel deep-learning model designed to improve the diagnosis and classification of malaria-infected cells. The proposed architecture integrates stacked Convolutional Neural Networks (CNNs) for spatial feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for modeling temporal dependencies. To enhance interpretability and performance, MalNet-DAF incorporates dynamic attention mechanisms. The Spatial Attention Module (SAM) highlights critical disease-affected regions by refining CNN-extracted features, while the Temporal Attention Module (TAM), applied after Bi-LSTM processing, emphasizes informative time steps by suppressing irrelevant signals. The fused spatial and temporal feature vectors are processed through Dense Layers with ReLU activation and Dropout regularization. The model is trained and validated using a publicly available malaria dataset from the National Institutes of Health (NIH). Experimental results show that MalNet-DAF achieves a classification accuracy of 99.24%, outperforming traditional baseline models. These findings demonstrate the potential of dynamic attention-driven deep learning (DL) in supporting real-time clinical decision-making and addressing challenges in healthcare diagnostics.