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

Naruenatthanaset, Korranat; Chalidabhongse, Thanarat H.; Palasuwan, Duangdao; Anantrasirichai, Nantheera; Palasuwan, Attakorn

doi:10.48550/arxiv.2012.01321

Cited by 2 publications

(6 citation statements)

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“…This section describes the work related to RBC morphology. Korranat et al 3 split the overlapped RBC cells using concave point analysis and ellipse fitting methods. In the second stage, an imbalance class of multiple RBCs abnormality is classified using a deep EfficientNet-B1 classifier with a data augmentation technique.…”

Section: Related Studymentioning

confidence: 99%

“…In the second stage, an imbalance class of multiple RBCs abnormality is classified using a deep EfficientNet-B1 classifier with a data augmentation technique. In, 3 a total of 11 abnormal RBC categories, one normal category, and one uncategorized RBC category has been taken for analysis. Kitsuchart papa et al 4 used a semi-supervised GAN deep learning method to solve the data labeling issue.…”

Section: Related Studymentioning

confidence: 99%

“…The deep learning 3 approach has been used to classify blood cells. To categorize the disease, a deep learningbased model requires more data than typical machine learning techniques.…”

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confidence: 99%

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HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

Dhar

Kothandapani

Satti

et al. 2023

Int J Imaging Syst Tech

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Red blood cells (RBCs) are the more crucial components of a healthy organism.Poikilocytosis is a disorder in which blood cells lose their standard shapes. The human bodies are affected by disorders like anemia and thalassemia because the structure of RBCs shape changes. Hematologists take longer time to detect abnormalities in RBCs. The proposed hyper-parameter optimizable K nearest neighbor (HPKNN) algorithm detects and classifies numerous types of poikilocytosis quickly and accurately. On the Chula-PIC-Lab and Erythrocytes IDB datasets, the proposed HPKNN algorithm was tested. After the preprocessing stage, the Freeman chain code has been utilized to retrieve the exact boundary shape of RBC morphology. On datasets of Erythrocytes IDB 1, 2, 3, and Chula-PIC-Lab(Chula-RBC-12), the morphological tools and Freeman chain code based segmentation provide 97.44% and 94.45% accuracy, respectively, which is superior than other methods. The proposed HPKNN classifier has an overall sensitivity of 97.9%, precision of 97.9%, and an f 1 score of 97.9% on the Chula-PIC-Lab (Chula-RBC-12) dataset to classify 10 classes, namely acanthocyte, dacrocyte, echinocyte, elliptocyte, normal, overlapped, schistocyte, spherocyte, stomatocyte, and target cell, which is greater than any other current approach. The HPKNN classifier achieves 98.085% precision, 98.085% sensitivity or recall, and 98.085% f 1 -score values on the Erythrocytes IDB datasets to classify circular, elongated, and other RBC classes.anemia, Freeman chain code, KNN classifier, poikilocytosis | INTRODUCTIONPoikilocytosis means the RBC blood cells are abnormal due to the changes in their standard shape. 1 The normal RBC blood cells look disk-shaped, biconcave, and trim. The RBC blood smear cells have no nucleus, lysosome, mitochondria, and endoplasmic. The diameter of normal RBC cells is 6.2 to 8.2 μ. 2 Generally, it is observed that the diameter of normal RBC cells is 7.2 μ. 2 The RBC cells are responsible for carrying different nutrients and oxygen to our body's organs and tissues. When RBCs' shape varies from normal, they are not having enough nutrients and oxygen. We are suffering from lots of diseases because of a lack of oxygen and nutrients. Poikilocytosis affects the body due to trauma or abnormalities occurring in the membrane. The poikilocytosis abnormality that occurs due to trauma is blister and keratocytes, teardrop cells, schistocytes, pyro-poikilocytes, and semilunar bodies. Poikilocytosis due to abnormalities in the membrane are target cells, burr cells, mouth

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Section: Related Studymentioning

confidence: 99%

Section: Related Studymentioning

confidence: 99%

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HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

Dhar

Kothandapani

Satti

et al. 2023

Int J Imaging Syst Tech

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“…Our dataset were provided by Chulalongkorn University (Naruenatthanaset et al, 2021) with total 591 microsopic images of RBCs. Three haematologists checked each cell and the majority vote was used as the label.…”

Section: The Datamentioning

confidence: 99%

“…The issue with an imbalanced dataset is that the model will be very good at recognising the majority classes, as it will have been trained with a lot of information about these classes, and worse at recognising the minority classes which it has less information about. The most common techniques used in deep learning is data augmentation in data space (Anantrasirichai et al, 2018;Naruenatthanaset et al, 2021). These however cannot be used with SVM or when a size of dataset is very small or the data distribution is highly skewed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vision-based Abnormal Red Blood Cell Classification

Wong,

Anantrasirichai,

Chalidabhongse

et al. 2021

Preprint

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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.

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

Cited by 2 publications

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HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

Analysis of Vision-based Abnormal Red Blood Cell Classification

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