Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

Suryani, Esti; Wiharto, Wiharto; Palgunadi, Sarngadi; Pradana, TP Nurcahya

doi:10.1088/1742-6596/801/1/012044

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…Another study was conducted to classify AML M0 and AML M1 on the results of WBC segments with RGB to YCbCr conversion using the k-NN classification method, obtaining an accuracy of 59.87% [12]. Then the classification of AML M2 and AML M3 diseases with the Momentum backpropagation method from the results of WBC segmentation with the Watershed Distance Transform method obtained an accuracy of 94.285% [26]. While the classification on AML M1.…”

Section: B Discussionmentioning

confidence: 99%

Detection of Acute Myeloid Leukemia based on White Blood Cell Morphological Imaging using Naïve Bayesian Algorithm

Suryani¹,

Wiharto²,

Putra³

et al. 2021

IJACSA

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Section: B Discussionmentioning

confidence: 99%

Detection of Acute Myeloid Leukemia based on White Blood Cell Morphological Imaging using Naïve Bayesian Algorithm

Suryani¹,

Wiharto²,

Putra³

et al. 2021

IJACSA

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“…The stopping term does not depend on the gradient of the image but related to a particular segmentation of the image [1,5]. Having obtained the image of white blood cells intact, overlapping cells were separated by using Watershed Distance Transform [19,24]. The result of the WBC body segmentation was a binary mask which was then pixelmultiplied with the corresponding pixel in original image to get the WBC object in RGB channel.…”

Section: Image Enhancement and Segmentationmentioning

confidence: 99%

Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

et al. 2018

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Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.

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“…The image preprocessing stage, the stage of preparing image objects to be subjected to feature extraction and parameter calculation, is a very crucial step in a pattern recognition system. The accuracy of the object segmentation results from a method to ground truth makes it feasible to be used in a particular case (Suryani, Wiharto, Palgunadi, & Prakisya, 2017), Leukemia or which can be called blood cancer is a dangerous disease that attacks the human body due to the uncontrolled growth of white blood cells (Sachin & Kumar, 2017). Bone marrow works continuously to form excess blood cells.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

Prakisya

Setiawan

2021

IJIE

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Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms

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Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

Cited by 15 publications

References 2 publications

Detection of Acute Myeloid Leukemia based on White Blood Cell Morphological Imaging using Naïve Bayesian Algorithm

Detection of Acute Myeloid Leukemia based on White Blood Cell Morphological Imaging using Naïve Bayesian Algorithm

Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

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