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

• Published in: arXiv.org
• Main Authors: Malhotra, Rishabh, Joshi, Dhron, Ku Young Shin
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 22.05.2020
• Subjects: Algorithms; Artificial neural networks; Classification; Deep learning; Diagnostic systems; Empirical analysis; Identification methods; Image classification; Machine learning; Malaria; Neural networks
• ISSN: 2331-8422

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%).