Normal and abnormal red blood cell recognition using image processing

Abdulkarim, Hajara Aliyu; Sudirman, Rubita; Razak, Mohd Azhar Abdul

doi:10.11591/ijeecs.v14.i1.pp96-100

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…This architecture achieved an average accuracy of 93%. In Aliyu et al (2019) , normal and abnormal RBCs were identified by leveraging Form Factor, Perimeter, and area features, achieving an accuracy of 94%. However, the method was ineffective when dealing with noisy images.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

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Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

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Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

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“…Likewise, through images, you can also detect diseases such as anemia. A method was proposed to recognize images of normal and abnormally shaped red blood cells using the shape factor, perimeter, and area as feature descriptors [9]. Also, the design of a finger probe for non-invasive diagnosis of anemia in children, had a satisfactory result, for which several tests were performed, in which the detection of anemia depended basically on blood cells [10].…”

Section: Introductionmentioning

confidence: 99%

Mobile application for the control process of childhood anemia in time of the pandemic

Mancisidor-Bazán,

Morales-Guillén,

Cabanillas-Carbonell

2023

IJPHS

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The objective of the research was to implement a mobile application for the control process of childhood anemia in times of pandemic, in order to have better medical control with faster care, better control of the diagnosis of anemia in children, and better performance and satisfaction. The following Scrum method was applied. The research has a quantitative approach, of experimental type. The sample consisted of 40 children. Results were obtained regarding time control in the speed of care, an increase of 10.2% was obtained; regarding the number of diagnoses, control, and follow-up in recovery there was an increase of 33.3% and finally regarding the control of performance and satisfaction with care there was an increase of 73.3%.

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“…In addition, to automatically classify the types of abnormal RBCs, information on various features of RBCs quantified through mathematical formulas, support vector machine (SVM), and decision tree (DT) classifier models is utilized (Gulati et al, 2013). In previous studies related to the foregoing the amount of computation increases, and the classification time is relatively long because the RBCs are categorized through formula calculation (Aliyu et al, 2019; Divina et al, 2020; Gulati et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Gil

Moon

Lee

et al. 2022

Microscopy Res & Technique

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The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).

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Normal and abnormal red blood cell recognition using image processing

Cited by 6 publications

References 6 publications

Blood cell image segmentation and classification: a systematic review

Blood cell image segmentation and classification: a systematic review

Mobile application for the control process of childhood anemia in time of the pandemic

Automatic analysis system for abnormal red blood cells in peripheral blood smears

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