Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection

Mishra, Sonali; Majhi, Banshidhar; Kumar, Pankaj

doi:10.1016/j.bspc.2018.08.012

Cited by 121 publications

(72 citation statements)

References 15 publications

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“…6. Many conventional supervised learning methods have been used to classify leucocytes in microscopic blood smear images, such as Support Vector Machine (SVM) [32][33][34], Naive Bayes (NB) [35][36][37], K-Nearest Neighbor (KNN) [38][39][40], and Artificial Neural Network (ANN) [41][42][43]. Some popular WBCs nuclei detection techniques are identified and reviewed, which are presented in Table 2.…”

Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

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Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

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“…Fringe cells that were situated at extraordinary corners are not precisely managed and need to exact extraction of outskirt cells and furthermore the sub arrangement of acute lymphoblastic leukemia into its particular phenotypes. Sonali Mishra et al [3] structured a texture highlight put together arrangement with respect to minuscule blood smear for acute lymphoblastic leukemia detection. They present a powerful plan for grouping of the ordinary white platelets from the influenced cells in a minute picture.…”

Section: Background Surveymentioning

confidence: 99%

“…The skirmishing potential of the human body is reduced with the increase in the number of malignant WBCs and the foreign material gets diminished. Detection and classification of ALL in early stage can considerably get better chance of recovery, particularly in the case of 5 to 10 years old children [3]. The detection and categorization of blast blood cell (also known as unhealthy WBCs) in the human bone marrow is also an imperative footstep for the prevention from ALL hematopoietic disease.…”

Section: Introductionmentioning

confidence: 99%

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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“…Several machine learning-based computer-aided ALL diagnosis methods have been presented over the last few years (Mohapatra et al, 2011;Madhukar et al, 2012;Joshi et al, 2013;Putzu et al, 2014;Mohapatra et al, 2014;Chatap & Shibu, 2014;Neoh et al, 2015;Reta et al, 2015;Vincent et al, 2015;Patel & Mishra, 2015;Kazemi et al, 2015;Amin et al, 2016a,b;Singhal & Singh, 2016;Rawat et al, 2017a,b;Mishra et al, 2017;Karthikeyan & Poornima, 2017;Mishra et al, 2019). All these methods utilize a predefined set of features based on the structure of the nucleus or cytoplasm of the cells to train classifiers such as naïve Bayes, decision tree, support vector machine (SVM), random forest, and the ensemble of classifiers for the diagnosis of ALL.…”

Section: Introductionmentioning

confidence: 99%

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Gehlot

Gupta

2020

Medical Image Analysis

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Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet θ is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8% is obtained that is best so far on this challenging dataset.

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Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection

Cited by 121 publications

References 15 publications

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

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

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

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