Peripheral blood smear analysis using image processing approach for diagnostic purposes: A review

Hegde, Roopa B.; Prasad, Keerthana; Hebbar, Harishchandra; Sandhya, I

doi:10.1016/j.bbe.2018.03.002

Cited by 38 publications

(19 citation statements)

References 35 publications

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“…Thick and thin blood smears are a detected feature of red blood cells (RBCs) shown in blood films, revealing features such as the color, size, texture, morphology and position of the parasite from the malaria patient. They represent the most popular method for the diagnosis of malaria for all clinics, hospitals and medical laboratories because they represent an inexpensive method for the diagnosis of an endemic disease such as malaria [ 12 , 13 ]. Figure 1 presents the dipstick method.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

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Section: Introductionmentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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“…Classification of results clearly shows that the algorithm was in total agreement with the testing dataset up to 98% giving a percentage error of 2%. Furthermore, the results obtained reveals that not all overlapping red cells are sickle cells as stated in the existing algorithms [9][10][11][12].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…Furthermore, in a study by R. Desai and H.G. Virani [11], a circular Hough transform technique was used to develop a method for the detection of RBC from blood smears.…”

Section: Introductionmentioning

confidence: 99%

An algorithm to detect overlapping red blood cells for sickle cell disease diagnosis

Vicent

Kawuma

Yonasi

2022

IET Image Processing

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In Africa, Uganda is among the countries with a high number of babies (20,000 babies) born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anaemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied on blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cells images as inputs to detect if these cells are sickle cell anaemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity and specificity were 98.18%, 98.29% and 97.98% respectively.

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“…Abnormalities in the physical conformation of the cells can lead to various medical conditions due to an insufficient supply of oxygen to tissues. Typically, a Haematologist will visually examine a microscopic image to detect abnormal RBCs, a time-consuming and subjective process (Hegde et al, 2018;Aime et al, 2019). This paper proposes to automate this process using machine learning technologies, thus increasing throughput of disease detection, reducing diagnosis time and potentially introducing some standardisation to the practice (Aime et al, 2019).…”

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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Peripheral blood smear analysis using image processing approach for diagnostic purposes: A review

Cited by 38 publications

References 35 publications

Analyzing Malaria Disease Using Effective Deep Learning Approach

Analyzing Malaria Disease Using Effective Deep Learning Approach

An algorithm to detect overlapping red blood cells for sickle cell disease diagnosis

Analysis of Vision-based Abnormal Red Blood Cell Classification

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