Automatic Malaria Parasite Detection Using Machine Learning and Image Classification

Microscopic examination of stained blood smears remains a reference method for malaria diagnosis, but is labour-intensive and operator-dependent. We evaluated image-based machinelearning models for parasite detection in single-cell images and report a deployment-oriented configuration. We used the NIH “Cell Images for Malaria” set (27,558 labelled cells; 13,780 parasitised; 13,778 uninfected). We compared a lightweight CNN against SVM and Naïve Bayes baselines. Variants with HOG features and dimensionality reduction (PCA/LDA) were explored for analysis only. The best CNN achieved 95.0% accuracy, 94.7% precision, 95.3% recall, and 95.0% F1 on the held-out test set, with a ROC-AUC of 0.91. We showed that a compact CNN trained on publicly available cell images yields high diagnostic performance and can be embedded in a simple desktop GUI for point-of-care assistance.