Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users

TL;DR

This paper introduces a crowdsourcing approach using non-expert workers to accurately label blood smear images for Sickle Cell Disease, enabling dataset annotation for automated diagnosis tools.

Contribution

It demonstrates that non-expert crowdworkers can reliably annotate blood smear images with high consensus, supporting scalable dataset creation for SCD diagnosis.

Findings

High accuracy achieved with consensus among crowdworkers

Crowdsourcing reduces reliance on expert labeling

Potential for training automated diagnostic algorithms

Abstract

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD