Acute lymphoblastic leukemia segmentation using local pixel information

Al-jaboriy, Saif S.; Sjarif, Nilam Nur Amir; Chuprat, Suriayati; Abduallah, Wafaa Mustafa

doi:10.1016/j.patrec.2019.03.024

Cited by 61 publications

(33 citation statements)

References 27 publications

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“…More experiments are also conducted to compare the proposed approach to the systems that are based on deep learning such as in [24–26], see Table 6.…”

Section: Resultsmentioning

confidence: 99%

“…As the authors claimed, this technique reached an accuracy of 97.18%. Saif et al introduced a system based on machine learning approach and image processing technique, and hence, the characteristics of blast cells were extracted using four‐moment statistical features and artificial neural networks (ANNs) [26]. This system is reached an accuracy rate of 97% using ALL‐IDB data set.…”

Section: Literature Reviewmentioning

confidence: 99%

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Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.

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“…More experiments are also conducted to compare the proposed approach to the systems that are based on deep learning such as in [24–26], see Table 6.…”

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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“…The comparison of proposed work with some other existing work, which is considered in scrutiny of proposed work, is described in below table. Authors Accuracy (%) Al-jaboriy [1] 97.01 Vasundhara Acharya [2] 93.15 Sonali Mishra [4] 91.32 Sachin Kumar [9] 95.74 Hossain Abedy [13] 96.35…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we present the survey of existing work based on the ALL detection and classification using different algorithms and techniques. Saif S Al-jaboriy et al [1] had proposed an acute lymphoblastic leukemia segmentation utilizing neighborhood pixel data. Creators have received diverse computational techniques to distinguish the idea of impact cells; be that as it may, these strategies are unequipped for precisely fragmenting leukocyte cells because of some real weaknesses, for example, absence of complexity among articles and foundation, affectability to dim scale, affectability to clamor in pictures, and huge computational size.…”

Section: Background Surveymentioning

confidence: 99%

“…Human blood samples visual assessment is a most important decisive factor for the detection and classification of leukemia from microscopic images [1]. There are two different types of Leukemia first in known as Acute Lymphoblastic Leukemia (ALL) and second is Acute Myeloid Leukemia (AML) which can go ahead to human death if not treated at the accurate time [2].Myeloid organs of human are affected by AML, whereas bone marrow is affected by the ALL types of leukemia.…”

Section: Introductionmentioning

confidence: 99%

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Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Sharma*¹,

Buksh²

2019

IJEAT

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Blood cell malignantly growth has been accounted for to be one of the most transcendent types of disease maladies. ALL (Acute Lymphoblastic Leukaemias) is the malignant types of blood cancer and their detection and classification in earlier stage is biggest issue. Automatic detection and classification of ALL from microscopic images is a challenging and intellectual assignment in medical science. Existing techniques for ALL detection and classification are an understandable alternative for real-time dermoscopic data analysis. Existing microscopic image processing approaches are unable to analyze the ALL data with non-stationary nature. In this perspective, the focus of this research is to design hybrid Convolutional Neural Network (CNN) architecture by utilizing Firefly Optimization Algorithm (FOA/FFA) to detect the ALL from microscopic images of human blood cell into malignant or normal blood cell. Methods: For training and testing of proposed ALL Detection and Classification (ALL-DC) Model, Standard ALL-IDB (Acute-Lymphoblastic-Leukaemias Image Database for Image Processing) is used with hybrid CNN architecture based on the FOA. Here, Histogram of Oriented Gradients (HOG) descriptor with FOA is used as feature extraction and selection mechanism from the Region of Blood Cell (ROBC).Feature extraction approach plays an important responsibility to classify lots of blood diseases. On the way to achieve this goal, we proposed ALL-DC model that combines recent developments in deep learning with fuzzy based CNN structure and for ROBC segmentation, hybridization of K-means segmentation algorithm with FOA that are capable to segment the accurate blood cell region from microscopic images. Using k-means segmentation technique, the foreground and background component is separated into two regions and after that to improve the segmentation results; FOA is used with the novel concept of image enhancement approach. Results: The proposed ALL-DC system is evaluated using the largest publicly accessible standard ALL-IDB dataset, containing 600 training and 400 testing microscopic images. When the evaluation parameters of proposed work is compared with a number of other state-of-art schemes, the proposed scheme achieves the most excellent performance of 98.5% in terms of accuracy which also known as area under the curve (AUC) in differentiating ALL from benign cell using only the extracted and optimized HOG feature. Conclusion: When the proposed model is tested on different microscopic images, evaluation parameters is calculated and compared with a few other state-of-art methods and we obtained the proposed method achieves the best performance in terms of classification accuracy. ALL-DC model is implemented and constructed using the concept of Image

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A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Ghaderzadeh

Aria

Hosseini

et al. 2021

Int J of Intelligent Sys

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The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from noncancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, Mobile-NetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and Dense-Net201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved

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Acute lymphoblastic leukemia segmentation using local pixel information

Cited by 61 publications

References 27 publications

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

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