The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Rahmat, R F; Wulandari, Fitriah; Faza, Sharfina; Muchtar, M A; Siregar, Ikhsan

doi:10.1088/1757-899x/308/1/012015

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…We found that RCNN + CNN has given the lowest prediction accuracy of about 72% for multi-class classification. Whereas, the adaptive neuro-fuzzy inference system (ANFIS) [12], CNN based you only look once (YOLO) [13], self-organized map [14] and ResNet50 [15] has similar accuracy as our proposed model. To validate and evaluate the performance of the proposed system, we have tested it on different publicly available datasets.…”

Section: Resultsmentioning

confidence: 90%

Red Blood Cell Classification Using Image Processing and CNN

Parab¹,

Mehendale

2021

SN COMPUT. SCI.

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In the medical field, the analysis of the blood sample of the patient is a critical task. Abnormalities in blood cells are accountable for various health issues. Red blood cells (RBCs) are one of the major components of blood. Classifying the RBC can allow us to diagnose different diseases. The traditional, time-consuming technique of visualizing RBC manually under the microscope, is a tedious task and may lead to wrong interpretation because of the human error. The various health conditions can change the shape, texture, and size of normal RBCs. The proposed method has involved the use of image processing to classify the RBCs with the help of convolution neural networks. The algorithm can extract the feature of each segmented cell image and classify it into 9 various types. Images of blood slides were collected from the hospital. The overall accuracy was 98.5%. The system has been developed to provide accurate and fast results that can save patients' lives.

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Section: Resultsmentioning

confidence: 90%

Red Blood Cell Classification Using Image Processing and CNN

Parab¹,

Mehendale

2021

SN COMPUT. SCI.

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“…The Seg-SOM workflow consists of three primary steps: (1) nuclear segmentation, (2) nuclei grouping based on their shape and size performed by the self-organizing map algorithm combined with hierarchical clustering of SOM nodes, and (3) in silico cell type staining. SOMs have been previously used in digital pathology for red blood cell classification (26), megakaryocyte subtypes clustering (27), and analyzing 3D cell surface information (28). This work is the first to present the combination of SOMs and NMF as a general tool for dimensionality reduction of nuclear morphology, the grouping of nuclei in complex tissues, discovering nuclear subtypes, nuclear in silico labeling, and extracting machine learning features as potential spatial biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

et al. 2021

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Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&E-stained tissue.

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“…If the value of form factor is equal to 1, then it is said to be normal cell having 0.9 value is also adjustable [18]. Normal cells are round in shape, for this purpose roundness is measured, having 0.9 or 0.8 roundness value is said to be normal cells [19]. Surface area of normal cells are smooth, when cells having even a small amount of infection the area of those cells becomes rigid [20].…”

Section: Expected Values Of Uninfected Cellsmentioning

confidence: 99%

Blood image analysis to detect malaria using filtering image edges and classification

et al. 2019

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Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently.

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The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Cited by 13 publications

References 5 publications

Red Blood Cell Classification Using Image Processing and CNN

Red Blood Cell Classification Using Image Processing and CNN

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

Blood image analysis to detect malaria using filtering image edges and classification

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