Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.

Accurate and early diagnosis of malaria from peripheral blood smear images remains a critical challenge in healthcare, particularly in resource-limited settings. In this work, we propose an optimized convolutional neural network (CNN) framework enhanced by Otsu thresholding-based image segmentation for improved detection of malaria-infected cells. A dataset of 43,400 blood smear images was utilized, divided into a 70:30 ratio for training and testing. A baseline 12-layer CNN achieved 95% accuracy, which improved to 97% with the integration of EfficientNet-B7 through a hybrid parallel feature-fusion model. Further enhancement was achieved using Otsu-based segmentation, where preprocessing emphasized parasite-relevant regions while retaining morphological context in the RGB images. This approach yielded the highest accuracy of 97.96%, reflecting a ~ 3% gain over the baseline CNN. To ensure the reliability of the segmentation step, we created a manually annotated subset of 100 images and computed quantitative segmentation metrics by comparing Otsu-generated masks with reference masks. The method achieved a mean Dice coefficient of 0.848 and Jaccard Index (IoU) of 0.738, confirming that Otsu segmentation effectively isolates parasitic regions despite its simplicity. Five-fold cross-validation was also performed, yielding consistent results (94.8%, 96.9%, and 97.8%), thereby supporting the robustness of the framework. The proposed pipeline demonstrates that simple yet effective preprocessing can significantly boost CNN-based classification while maintaining interpretability and computational feasibility. These findings suggest that segmentation-driven deep learning frameworks can play a vital role in developing reliable, scalable, and cost-effective malaria diagnostic tools.