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AI Case Study

Anglia Ruskin University researchers develop mobile system which detects tuberculosis with 98.4% accuracy

Researchers from Anglia Ruskin University test different machine learning methods to classify digital images created using biosensors for the presence of tuberculosis antibodies. The goal was to create a system which could process and classify the images on a portable phone. They were able to achieve a 98.4% accuracy doing so using a random forest method.



Healthcare Providers And Services

Project Overview

The researchers developed a system "to ‘automatically’ detect TB-specific antibodies by analysing digital images (i.e. ELISA images) with colour signals produced by biosensor technology. The plasmonic ELISA tests were conducted in Universiti Putra Malaysia. The proposed system does not require any additional hardware such as an opto-mechanical attachment to enhance the colour detection or guide the illumination source, which makes the system the most conveniently portable. Utilising an intelligent image processing algorithm, the presented system robustly separates the samples from the assay plate and extracts the features, and within a few seconds the system predicts the class label via a machine learning algorithm with high accuracy and ease of use. The application was tested on Samsung Galaxy S6, Samsung Galaxy Note 3 and Samsung Galaxy J3 Prime."

Reported Results

The results indicated that the machine learning methods explored for classifying images as positive or negative for TB were more successful than other methods. They achieved over 95% accuracy, with the Random Forest method providing the best result based on ROC (tradeoff between false positives and false negatives). "On the mobile platform, for this unseen data, the system provided correct prediction for 60 samples. Thus, a final accuracy i.e. from image processing up to TB detection on the mobile platform, of 98.4% was achieved"


"The non-parametric classifiers such as random forest (RF), decision tree, k-nearest neighbours algorithm (kNN), and cubic support vector machine (CSVM) performed better than the parametric classification method e.g. linear discriminant and logistic discrimination. Without cross-validation, all these non-parametric methods produced 100% accuracy. The Multilayer Perceptron (MLP) with backpropagation was comparatively slow and the classification performance was poor as well. It provided 95.2% accuracy. In this paper, the RF and Random Committee (RC) both showed 98.9% accuracy with stratified cross validation (10-fold) in the Weka platform. Keeping the batch size, number of seeds, minimum number of instances per leaf as same as RF, the RC was built in 0.01 s using default number of iterations (10). The size of the tree varied in each iteration. The Random Tree (RT), a decision tree built on a random subset of columns achieved 98.4% accuracy. Keeping the parameters as same as RC, the Bagged Trees consisting unpruned binary trees provided 95.7% accuracy. Considering the ROC area, the RF is the best classifier for our dataset."


Digital Data

Digital Data Management


"Tuberculosis (TB) is a communicable disease, infecting one third of the world's population. In 2015, 1.8 million TB-related deaths were reported (Centers for Disease Control and Prevention, 2017)... However, TB is curable with appropriate early diagnosis. The most common diagnosis procedure for TB is a skin test (Mantoux test) or a blood test (Centers for Disease Control and Prevention.; NHS, 2017). Despite many commercial test schemes, there is still a need for an easy-to-use, effective and feasible point-of-care (POC) TB diagnosis tool, particularly for the remote community where there are very limited or no diagnostic facilities. Such a tool should possess the following features: low cost mobile solution, anytime anywhere access, low energy consumption, ease of use, fast and automatic identification of TB."



"The dataset contains images of 96 wells, which are partially filled, which means the plates contain both empty wells in addition to wells filled with sample. The final selection of 27 images from 22 independent observations were taken in a laboratory lighting environment. These images contain 266 samples - 81 of them are positive for TB-specific antibody, 181 are negative and three of the samples failed to produce any indicative result, thus 263 samples were finally selected. A mobile phone holder (NJS Telescopic Music Record Mobile Phone iPad iPhone Stand Inc G Clamp Mount 68 G) was used while capturing the image. However, the acquired images vary in terms of well size, camera to ELISA plate position, light exposure and mobile phone. Considering a robust application, this variation is expected in the real life incoming images.
To test the efficiency of this mobile-based intelligent algorithm for detecting TB, a separate dataset was used than [above]. This new dataset is unknown to the system and contained 61 samples. Among these samples, 20 were positive, 41 were negative and one failed to produce a colour. This held-out validation on the mobile platform ensures the reliability of the system."

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