Leveraging Multiple CNNs for Triaging Medical Workflow

TL;DR

This paper introduces a multi-CNN system using VGG16 models to assign a critical index to skin disease images, improving triage efficiency by prioritizing critical cases in medical workflows.

Contribution

It presents a novel conglomerate neural network architecture that outputs a continuous critical index, enhancing triage precision over traditional binary classification.

Findings

Reorders images from most to least critical accurately

Demonstrates promising results in prioritizing critical cases

Improves triage workflow efficiency

Abstract

High hospitalization rates due to the global spread of Covid-19 bring about a need for improvements to classical triaging workflows. To this end, convolutional neural networks (CNNs) can effectively differentiate critical from non-critical images so that critical cases may be addressed quickly, so long as there exists some representative image for the illness. Presented is a conglomerate neural network system consisting of multiple VGG16 CNNs; the system trains on weighted skin disease images re-labelled as critical or non-critical, to then attach to input images a critical index between 0 and 10. A critical index offers a more comprehensive rating system compared to binary critical/non-critical labels. Results for batches of input images run through the trained network are promising. A batch is shown being re-ordered by the proposed architecture from most critical to least critical roughly accurately.