Classify AI
Classify AI is a software package designed to train, optimise and evaluate machine learning classifiers for automated reading of HIV Rapid Diagnostic Tests (RDTs). It compares the performance of 4 different approaches (MobileNet_v2, MobileNet_v3, ResNet50 and SVM), using a dataset of images captured in the field in rural South Africa.
Product Specification
Classify AI is a series of interdependent scripts written in Python 3.6.
Description
This repository contains the code used to produce the results presented in the following publication:
Deep learning of HIV field-based rapid tests DOI:10.1038/s41591-021-01384-9
The work presented is the result of a collaboration between i-sense (https://www.i-sense.org.uk/) and the Africa Health Research Institute (AHRI, https://www.ahri.org/).
The datasets are available upon request, please see details in the publication.
The repository is divided in 2 subfolders corresponding to Figures 3 and 4 of the publication, respectively.
In Figure 3, the others used a dataset of 11, 374 images of HIV RDTs captured in the field, around AHRI. They trained and optimised classifers, performed cross-validation to check for reproducibility. They produced 2 optimal classifers, one for each type of HIV RDT used in the study.
In Figure 4, the authors conducted a pilot study to assess 1) the level of agreement between participants when intepreting HIV RDTs by eye; 2) the performance of their optimal classifers (one for each type of HIV RDT used in the study) compared to traditional visual interpretation by humans.
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swap_vertical_circlemode_editAuthors (1)Prof Rachel McKendry
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swap_vertical_circlelibrary_booksReferences (1)
- V Turbé, C Herbst, T Mngomezulu, S Meshkinfamfard, N Dlamini, T Mhlongo, T Smit, V Cherepanova, K Shimada, J Budd, N Arsenov, S Gray, D Pillay, K Herbst, M Shahmanesh & R A. McKendry (17 June 2021), Deep learning of HIV field-based rapid tests, Nat Med
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swap_vertical_circlecloud_downloadSupporting documents (1)Product brochureClassify AI.pdfAdditional files may be available once you've completed the transaction for this product. If you've already done so, please log into your account and visit My account / Downloads section to view them.