Analysis of different Machine Learning and Deep Learning Techniques for Malaria Parasite Detection

Malaria is an epizootic illness caused by unicellular parasites. In Two thousand eighteen there were an estimated two hundred twenty-eight million cases of malaria worldwide. Conventional method of diagnosis requires experienced technician and careful perusal to classify between healthy and infected blood cell, which consumes a lot of time and is also prone to human error. With the help of ML and DL we can simulate human intelligence and make better predictions. The main aim of the paper is to compare the machine learning algorithms namely KNN, Decision Tree, Logistic regression and Random forest and implementing transfer learning with deep learning models VGG19, modified Resnet50 to improve the accuracy achieved with machine learning models thus proposing the best model for predicting malaria only by observing by blood cell image rather than doing any staining of blood, thus reducing any expert requirement.