A decision support system for Acute Leukaemia classification based on digital microscopic images

Negm, Ahmed S.; Hassan, Osama A.; Kandil, Ahmed H.

doi:10.1016/j.aej.2017.08.025

Cited by 86 publications

(38 citation statements)

References 21 publications

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“…As per our proposed technique, since the classification problem of lymphatic suspicious nodules needs significant effort to analyses the behaviors and properties at the micro-architectural levels. A leukemia segmentation approach [35] was proposed for classification of leukemia disease. The proposed algorithm uses watershed segmentation to visualize the WBC cells of blood and their correlations, whereas we visualize deepest knowledge of medullary thyroid cancer with approximated 99.00% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision Support System for Classification Medullary Thyroid Cancer

2020

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Due to the complex, heterogeneous and mimic morphological features of medullary thyroid cancer (MTC). It becomes often difficult to diagnose MTC at early stage. Since histopathological complex patterns of cancerous cells and tissues requires a huge effort to classify. Therefore thyroid cancer classification has become one of the significant research area area(s) of Machine Learning. We propose a decision support system to classify initial variation of morphological appearance of nuclei by using Convolutional Neural Networks (CNNs). The system comprises over three major layers, where image preprocessing techniques are used at top layer along with feature selection techniques. Classification model constructed by using CNNs at the second layer and result visualization described at third layer. Due to the unavailability of datasets for medullary thyroid cancer in literature, this research uses real-world datasets consisting upon 20GB cytological medical images and the approximated classification accuracy is measured about 99.00%. Malignant and non-malignant cells are visualized to assist the doctors in better way.

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Section: Literature Reviewmentioning

confidence: 99%

Decision Support System for Classification Medullary Thyroid Cancer

2020

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“…On active contour without the edge, it will minimize term fitting and add some term regulations, such as the length of the C curve and the area of the region in C. From the equation, the length of the curve can be measured by (4). While the area is formulated in (5). In other hand, the watershed segmentation method is a series of methods consisting of watershed transformation, distance transformation, morphological operations, and other related operations.…”

Section: = 4mentioning

confidence: 99%

“…The algorithm is used to separate the nucleus and cytoplasm in each sub-image. Negm [5] in his research, he used a decision support system that included panel selection and segmentation using K means clustering on some datasets on leukimia identification. Ali [6] proposed an algorithm to isolate and count lymphocytes in the image of white blood cells.…”

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confidence: 99%

Preliminary process in blast cell morphology identification based on image segmentation methods

Supriyanti

Wibowo

Firmanda

et al. 2020

IJECE

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The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

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“…In literature, various techniques have been proposed to detect ALL disease [1–19]. In [1], fuzzy‐based colour segmentation method is used for the separation of WBC from background which further utilises morphological and texture features for the accurate recognition of leukaemia cells.…”

Section: Introductionmentioning

confidence: 99%

“…However, the strength and classification accuracy can be further improved by utilising other significant features. Geometric, colour and texture features, when combined with the neural network (NN) model yield better accuracy [18]. However, the overall scheme is not computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

Automated acute lymphoblastic leukaemia detection system using microscopic images

et al. 2019

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An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre‐processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.

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A decision support system for Acute Leukaemia classification based on digital microscopic images

Cited by 86 publications

References 21 publications

Decision Support System for Classification Medullary Thyroid Cancer

Decision Support System for Classification Medullary Thyroid Cancer

Preliminary process in blast cell morphology identification based on image segmentation methods

Automated acute lymphoblastic leukaemia detection system using microscopic images

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