Orak Hücreli Anemi Tespiti Sickle Cell Anemia Detection

Albayrak, Batuhan; Darici, Muazzez Buket; Kiraci, Furkan; Öğrenci, Arif Selçuk; Özmen, Atilla; Ertez, Kerem

doi:10.1109/tiptekno.2018.8597092

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Elsalamony [35] has used the shape signature technique to detect two types of anemia then classify them using a neural network. Albayrak et al [36] have implemented CHT to distinguish between healthy cells and sickle cells. Chy et al [37] extracted features such as ratio, entropy, mean, standard deviation, and variance, which were used to train SVM to classify normal and sickle cells.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

et al. 2020

View full text Add to dashboard Cite

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The proposed method gives accuracy of 99.2% and 98.8% for SVM and ANN classifier with Scaled conjugate gradient BP algorithm, respectively. The performance of the system is also experimented by replacing NICK's thresholding with global Otsu's thresholding and results are shown in Table 3 Albayrak et al [9] 91.1 79 -92.9 Aliyu et al [10] 93 94 80 -Rakshit and Bhowmik [15] 95.8 ---…”

Section: Classification Using Ann and Svmmentioning

confidence: 99%

“…[5,6] Among the segmentation methods based on global thresholding, Otsu's thresholding is one of the popular method among the researchers for normal and abnormal RBC detection. [7][8][9] Aliyu et al have applied different segmentation techniques such as Otsu thresholding, Sobel, Laplacian of Gaussian and watershed on 30 SCA microscopic blood images and they obtained highest accuracy in Otsu's thresholding for SCA classification. [10] A system is presented that detects the types of RBC disorders using microscopic blood sample images.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method

Patgiri¹,

Ganguly²

2022

APJHS

View full text Add to dashboard Cite

Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.

show abstract