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Malaria is a deadly disease which claims the lives of hundreds of thousandsof people every year. Computational methods have been proven to be useful inthe medical industry by providing effective means of classification ofdiagnostic imaging and disease identification. This paper examines differentmachine learning methods in the context of classifying the presence of malariain cell images. Numerous machine learning methods can be applied to the sameproblem; the question of whether one machine learning method is better suitedto a problem relies heavily on the problem itself and the implementation of amodel. In particular, convolutional neural networks and k nearest neighboursare both analyzed and contrasted in regards to their application to classifyingthe presence of malaria and each models empirical performance. Here, weimplement two models of classification; a convolutional neural network, and thek nearest neighbours algorithm. These two algorithms are compared based onvalidation accuracy. For our implementation, CNN (95%) performed 25% betterthan kNN (75%).

Approaching Bio Cellular Classification for Malaria Infected Cells Using  Machine Learning and then Deep Learning to compare & analyze K-Nearest  Neighbours and Deep CNNs