Analysis of Vision-based Abnormal Red Blood Cell Classification

Abstract

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.

References in corpus (5)

• SMOTE: Synthetic Minority Over-sampling Technique
• A Survey on Deep Learning in Medical Image Analysis
• A systematic study of the class imbalance problem in convolutional neural networks
• Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
• Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset

Cited by in corpus (1)

• Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset