MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images

Abstract

Our study presents MozzieNet, a customized CNN model aimed at improving the identification of malaria parasites in blood smear microscopic images. By optimizing hyperparameters and incorporating techniques like data augmentation, batch normalization, and dropout, our model enhances robustness and generalization, addressing overfitting issues. Using the open‐source NIH malaria dataset with 27,558 images, we achieve a classification accuracy of 96.73%, recall rate of 97.90%, precision of 95.67%, area under the curve (AUC) of 99.35%, and F1 score of 96.77%. We performed feature maps and Grad‐CAM analysis on our proposed MozzieNet model to visualize and examine the targeted regions that are crucial for accurate predictions. Statistical analysis shows that the proposed architecture achieves promising performance and is superior to pre‐trained models and existing methods for malaria detection. MozzieNet is designed for cloud and low‐end smartphones, enabling malaria diagnosis in remote areas, thereby assisting physicians in informed malaria diagnosis and decision‐making.