  Identification of abnormalities in red blood cells (RBC) is key to diagnosing
a range of medical conditions from anaemia to liver disease. Currently this is
done manually, a time-consuming and subjective process. This paper presents an
automated process utilising the advantages of machine learning to increase
capacity and standardisation of cell abnormality detection, and its performance
is analysed. Three different machine learning technologies were used: a Support
Vector Machine (SVM), a classical machine learning technology; TabNet, a deep
learning architecture for tabular data; U-Net, a semantic segmentation network
designed for medical image segmentation. A critical issue was the highly
imbalanced nature of the dataset which impacts the efficacy of machine
learning. To address this, synthesising minority class samples in feature space
was investigated via Synthetic Minority Over-sampling Technique (SMOTE) and
cost-sensitive learning. A combination of these two methods is investigated to
improve the overall performance. These strategies were found to increase
sensitivity to minority classes. The impact of unknown cells on semantic
segmentation is demonstrated, with some evidence of the model applying learning
of labelled cells to these anonymous cells. These findings indicate both
classical models and new deep learning networks as promising methods in
automating RBC abnormality detection.
