Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size

TL;DR

This paper presents a deep learning-based multi-wavelength microscopy method for classifying malaria stages with limited labeled data, achieving high accuracy and efficiency compared to other CNNs.

Contribution

It introduces a novel multi-wavelength approach combined with a customized CNN for malaria stage classification using limited data, outperforming existing models in speed and accuracy.

Findings

Customized CNN performs comparably to well-known CNNs

Method achieves high classification accuracy with limited data

Proposed approach reduces computational time

Abstract

Malaria is a life-threatening mosquito-borne blood disease, hence early detection is very crucial for health. The conventional method for the detection is a microscopic examination of Giemsa-stained blood smears, which needs a highly trained skilled technician. Automated classifications of different stages of malaria still a challenging task, especially having poor sensitivity in detecting the early trophozoite and late trophozoite or schizont stage with limited labelled datasize. The study aims to develop a fast, robust and fully automated system for the classification of different stages of malaria with limited data size by using the pre-trained convolutional neural networks (CNNs) as a classifier and multi-wavelength to increase the sample size. We also compare our customized CNN with other well-known CNNs and shows that our network have a comparable performance with less computational time. We believe that our proposed method can be applied to other limited labelled biological datasets.