Malaria Cell Detection using Advanced SVM Techniques
S Shashikiran, H D Sunitha · 2024
Malaria, caused by Plasmodium parasites, is a blood disease carried via the bite of a female Anopheles mosquito. Instances of this occur nearly 240 million times in the coastal and rural regions of India. The disease affects about 40% of people annually. There is a threat to one-third of the world’s population. In general, macroscopic examinations are time-consuming. Examine both thin and thick blood smears to determine what causes a disease or condition and to identify risk factors in individuals. However, a smear’s accuracy is dependent on both its quality and the situation’s information. Cells with and without parasites are categorized and tallied. Manual assessment is the gold standard for diagnosis, yet it only yields 50% accuracy. It requires multiple steps to be finished. We will use various Support vector machine (SVM) algorithms for malaria cell detection, which provide varying degrees of accuracy and alleviate the time complexity difficulties associated with Deep learning methods. SVM-76%, SVM + t-distributed stochastic neighbor (t- SNE)-82%, SVM + Principal Component Analysis (PCA)-86%, and SVM in the Convolutional Neural Network (CNN) Model-96% are the outcomes of the various SVM approaches. This paper covers various Support Vector Machine (SVM) approaches for classifying malaria cells.