Florence B Tushabe, Samuel Mwesige, Vicent Kasule, Emily Nsiimire, Sarah C Musani, David Areu and Emmanuel Othieno
This article presents a method of sickle cell detection from microscopic images. We extract five attribute values from the connected components of an image, and train machine learning classifiers to recognize the sickle cells. Four classifiers were experimented with and the vest one was the K-Nearest neighbor classifier with 97.3% accuracy. The other classifiers are the Neutral network, Decision tree and Naïve Bayesian classifiers which resulted in accuracy rates of between 89-96.3%. This method is applicable for use in low cost computers since it is computationally cheap. The findings of this research can be considered as a screening method for diagnosing sickle cell aneamia.