Artificial Neural Networks for classifying the time series sensor data generated by medical detection dogs

Withington, Lucy; Diaz Pardo de Vera, David; Guest, Claire; Mancini, Clara and Piwek, Paul (2021). Artificial Neural Networks for classifying the time series sensor data generated by medical detection dogs. Expert Systems with Applications, 184, article no. 115564.

DOI: https://doi.org/10.1016/j.eswa.2021.115564

Abstract

The aim of this research was to discover if artificial neural networks can be used to classify pressure sensor data generated by medical detection dogs as they sniff biological samples. A detection dog can be trained to recognise the odour emitted by one of a wide range of diseases such as prostate cancer, malaria or, potentially, COVID-19. The dog searches a row of sample pots and indicates a positive sample by sitting in front of it. This offers a non-invasive means of diagnosing the specific cancer or disease that the dog has been trained to recognise. For this study, pressure sensors were attached to the sample pots to generate time series data pertaining to the dog’s searching behaviour as they press their nose against the sample pot to sniff its content. Automatic classification could provide a second form of indication, to support or refute the dog’s explicit signal (to sit at a positive sample), which is not always correct. Ultimately, classification software could eliminate the need for the dog to perform an indication gesture, making the dog’s task easier and training quicker.

Four different neural network architectures were evaluated: multilayer perceptron (MLP), a convolutional neural network (CNN), a fully convolutional network (FCN) and ResNet (a deep convolutional neural network). Each model was trained to classify the pressure data generated by medical detection dogs. To achieve a useful level of accuracy, it was found that the models needed to be trained using only those data samples where the dog had correctly classified the scent sample.

Model hyperparameters were tuned to improve accuracy. We found that the best performing model was MLP. When tested on previously unseen data, where the dog was not always correct, the classification performance of the MLP approached that of the medical detection dogs. For our particular dataset, the model’s true positive rate (i.e. recall) was 59%, matching that of the dogs. The model’s true negative rate was 79%, compared to the dogs’ 91%.

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