Malaria Detection Using Image Processing & Machine Learning

Malaria is a mosquito-borne blood illness caused by Plasmodium parasites that is lethal, contagious, and life-threatening. The most common and widely used method of diagnosing malaria is for skilled technicians to visually examine blood smears under a microscope for parasite-infected red blood cells.

This procedure is inefficient and time-consuming, and the diagnosis is dependent on the individual doing the examination's expertise and understanding. Our major goal is to develop a model that can detect cells from images of many cells in thin blood smears on typical microscope slides and categories them as infected or uninfected using image processing and Support Vector Machine (SVM), a machine learning classifier.

Keywords: Malaria, Blood smear images, Watershed segmentation, Machine learning, Support vector machine, Classification.