Deep learning-based classification of malaria through parasitized and uninfected blood smear image analysis

Abstract

Malaria remains a major global health issue, with over 229 million cases and 409,000 deaths reported annually, particularly in sub-Saharan Africa. Current diagnostic methods, such as microscopic examination of blood smears, are time-consuming and often lack accuracy due to human error and variability in slide quality. This study introduces Malaria-Net, a novel framework integrating advanced data preprocessing techniques with a Parasite Specific Attention Convolutional Neural Network (PSA-CNN) for enhanced feature extraction and Probabilistic Extremely Randomized Trees (PERT) for classification. The proposed approach begins with preprocessing steps, including image normalization, augmentation, and noise reduction to improve image quality and consistency. The PSA-CNN focuses on relevant features specific to malaria parasites, enhancing the network's ability to distinguish between different stages of infection. The PERT is then utilized for classification, leveraging its ability to handle high-dimensional data and provide probabilistic outputs. This method aims to improve diagnostic accuracy and reduce the reliance on manual interpretation, offering a more reliable and efficient solution for malaria detection. The proposed Malaria-Net achieves an accuracy of 99.937%, demonstrating its strong overall classification performance. It shows high precision (99.669%) and recall (99.337%), indicating that the model correctly identifies positive cases and minimizes false negatives. The F1-score of 99.539% reflects a balanced performance, combining precision and recall into a single metric, confirming its robustness in malaria detection.