Classification of cell types in Acute Myeloid Leukemia (AML) of M4, M5 and M7 subtypes with support vector machine classifier

Setiawan, Andika; Harjoko, Agus; Ratnaningsih, Tri; Suryani, Esti; Wiharto, Wiharto; Palgunadi, Sarngadi

doi:10.1109/icoiact.2018.8350822

Cited by 25 publications

(20 citation statements)

References 7 publications

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“…Setiawan et al [16] introduced a system that could classify the cells in AML of subtypes M4, M5, and M7. Firstly, the cells were segmented by a color k-means algorithm.…”

Section: Traditional Methodsmentioning

confidence: 99%

“…Data augmentation consists of three operations: translation, reflection, and rotation. In translation, the images are shifted along the X-axes and Y-axes with selected values being randomly bounded by the interval [15][16][17][18][19][20][21][22][23][24][25]. In the reflection process, the images are mirrored along the vertical axis.…”

Section: First Classification Modelmentioning

confidence: 99%

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Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

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Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.Computers 2020, 9, 29 2 of 12 and classification. However, the success of each step depends on the success of the preceding step. For example, the success of classification depends on the success of the preceding feature extraction, which itself depends on the success of the preceding segmentation. Hence, high classification accuracy requires the success of all steps, each of which is non-trivial and problem-dependent [1,7].Recently, deep learning achieved many breakthroughs in different fields such as computer vision, natural language processing, and object recognition. Deep neural networks, such as convolutional neural networks (CNNs), can be used effectively to build computer-aided diagnostic systems. However, the design and training of deep neural networks are non-trivial and time-consuming tasks. Hence, instead of building a deep neural network from scratch, we use the concept of transfer learning, in which a deep network that achieved success in solving a certain problem is tuned to solve another problem.This paper proposes two classification models that are based on transfer learning and can distinguish between healthy and unhealthy blood smear images with high accuracy. These models employ AlexNet, which is a deep CNN that achieved huge success in the image classification challenge, ImageNet 2012.The remaining sections of this paper are arranged as follows: Section 2 covers some of the traditional and end-to-end-based methods described in the literature. Section 3 presents our two proposed classification models. Section 4 discusses their implementation in our experiments. Section 5 discusses the results obtained. Finally, Section 6 concludes the paper.

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“…Setiawan et al [16] introduced a system that could classify the cells in AML of subtypes M4, M5, and M7. Firstly, the cells were segmented by a color k-means algorithm.…”

Section: Traditional Methodsmentioning

confidence: 99%

Section: First Classification Modelmentioning

confidence: 99%

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

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“…Currently, research related to medical imaging processing is growing rapidly. Some medical cases that can be helped with image processing are glaucoma [2], amniotic fluid [3], breast cancer [4], leukaemia [5]- [7], abnormality in red blood cell [8], [9], and many more. In research related to erythrocytes, there are several potential research topics to be developed.…”

Section: Introductionmentioning

confidence: 99%

Erythrocyte Classification using Multi Layer Perceptron, Naïve Bayes Classifier, RBF Network and SVM

Tyas,

Hartati,

Harjoko

et al. 2019

IJEAT

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Several diseases can be diagnosed based on the appearance of abnormal erythrocytes, among others anaemia and thalassemia. Process of examination peripheral blood smear manually is time-consuming and subjective. Currently, the process of examination peripheral blood smear by laboratory assistants can be assisted with digital image processing technology so that it can speed up the examination time and avoid subjectivity. This research begins with the process of microscopic image acquisition, then preprocessing, segmentation, feature extraction and classification. The microscopic image acquisition is carried out using an additional special camera on a microscope. In this study, we used peripheral blood smear of thalassemia patients and healthy individuals. We convert the RGB image to grayscale image and perform the median filtering in the preprocessing stage. In the segmentation stage, we used the watershed distance transform method. As a segmentation result, we got 7108 erythrocyte images consisting of nine types of erythrocytes. In feature extraction, we used shape, color and texture characteristics to represent erythrocytes. The combination of these three features is used as classifier’s input. One crucial stage in digital image processing technology is object classification. In this study, erythrocyte classification is done by comparing four types of the classifier to determine the best classifier performance in this case. Multi-Layer Perceptron (MLP), Naïve Bayes classifier, RBF Network, and SVM used as classifiers in this study. Experimental results showed that MLP got the highest performance with 89.6% accuracy, 89.3% precision and 89.6% recall. Furthermore SVM came in second place, followed by RBF Network and Naïve Bayes classifier.

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“…Klasifikasi sel darah putih juga dapat dilakukan secara otomatis yaitu dengan metode pengolahan citra digital. Beberapa penelitian juga telah dilakukan, salah satunya adalah klasifikasi sel darah menggunakan classifier SVM dengan fitur warna dan bentuk [4]. Selain itu multilayer perceptron juga digunakan sebagai classifier dengan fitur LDP sebagai ekstraksi cirinya [5].…”

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“…Pada evaluasi kinerja menggunakan confusion matrix yang berdasarkan nilai true positive, true negative, false positive, dan false negative. Dari hasil confussion matrix diperoleh performa sistem yang dihitung sesuai dengan persamaan (1) accuracy, persamaan (2) precission, persamaan (3) specitifity, persamaan(4)…”

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

Liyantoko

Candradewi

Harjoko

2019

Indonesian J. Electron. Instrum. Syst.

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Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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Classification of cell types in Acute Myeloid Leukemia (AML) of M4, M5 and M7 subtypes with support vector machine classifier

Cited by 25 publications

References 7 publications

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Erythrocyte Classification using Multi Layer Perceptron, Naïve Bayes Classifier, RBF Network and SVM

Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

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