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

TL;DR

This study compares machine learning and deep learning methods, specifically K-Nearest Neighbours and CNNs, for classifying malaria-infected cells, demonstrating CNN's superior accuracy in this medical imaging task.

Contribution

The paper provides a comparative analysis of KNN and CNN models for malaria cell classification, highlighting CNN's higher accuracy in this context.

Findings

CNN achieved 95% validation accuracy

KNN achieved 75% validation accuracy

CNN outperforms KNN by 25% in accuracy

Abstract

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