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.
Technology No. 26-062

Product Specification

Classify AI is a series of interdependent scripts written in Python 3.6.


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 ( and the Africa Health Research Institute (AHRI,

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.

  • swap_vertical_circlemode_editAuthors (1)
    Prof Rachel McKendry
  • swap_vertical_circlelibrary_booksReferences (1)
    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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Classify AI software Academic Licence 1.00

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