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

• URL: http://arxiv.org/abs/2012.01321v2
• Date: Wed, 9 Dec 2020 06:29:54 GMT
• Title: Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset
• Authors: Korranat Naruenatthanaset, Thanarat H. Chalidabhongse, Duangdao Palasuwan, Nantheera Anantrasirichai, Attakorn Palasuwan
• Abstract summary: Overlapping cells can cause incorrect predicted results that have to separate into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify red blood cells from blood smear images, specifically to tackle cell overlapping and data imbalance problems.
• Score: 1.7219362335740878
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated red blood cell classification on blood smear images helps hematologist to analyze RBC lab results in less time and cost. Overlapping cells can cause incorrect predicted results that have to separate into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify red blood cells from blood smear images, specifically to tackle cell overlapping and data imbalance problems. Focusing on overlapping cell separation, our segmentation process first estimates ellipses to represent red blood cells. The method detects the concave points and then finds the ellipses using directed ellipse fitting. The accuracy is 0.889 on 20 blood smear images. Classification requires balanced training datasets. However, some RBC types are rare. The imbalance ratio is 34.538 on 12 classes with 20,875 individual red blood cell samples. The use of machine learning for RBC classification with an imbalance dataset is hence more challenging than many other applications. We analyze techniques to deal with this problem. The best accuracy and f1 score are 0.921 and 0.8679 on EfficientNet-b1 with augmentation. Experimental results show that the weight balancing technique with augmentation has the potential to deal with imbalance problems by improving the f1 score on minority classes while data augmentation significantly improves the overall classification performance.

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