Deep Learning-Based Malaria Classification Using Single- and Dual-Branch CNN Architectures with Attention Modules
Simona Moldovanu, Gigi Tăbăcaru, Dan Munteanu, Marian Barbu · BioMedInformatics · 2026
Rapid and accurate diagnosis is crucial for the early detection of malaria caused by Plasmodium parasites. This study primarily focuses on identifying a suitable deep learning model for classifying malaria parasites. In an ablation process, we started with single-branch Convolutional Neural Network (SB-CNN) and dual-branch CNN (DB-CNN) architectures enhanced with attention mechanisms to improve feature representation. Specifically, we incorporate the Convolutional Block Attention Module (CBAM) to focus on both channel-wise and spatially important features. We also use the Efficient Channel Attention (ECA) module to capture local inter-channel relationships, while the Squeeze-and-Excitation (SE) block emphasizes globally significant feature maps, further improving the model’s ability to distinguish between classes. To identify the most effective model, we experimented with EfficientNet-B0 and EfficientNet-B3 as backbone networks and conducted an ablation study by integrating various attention modules, resulting in three DB-CNN variants. We applied t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the high-dimensional feature space between infected and uninfected samples. Utilizing the 5-fold cross-validation method on the Thick dataset, the best-performing models included architectures such as SB EfficientNet-B3, SB EfficientNet-B3 combined with CBAM, and DB EfficientNet-B3 across both branches, with CBAM in the first and SE in the second.