Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour

Chatap, Niranjan J.; Shibu, Sini

doi:10.9790/0661-16537987

Cited by 49 publications

(18 citation statements)

References 15 publications

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“…They are able to achieve 92% accuracy. Chatap et al 23 deployed global thresholding using Otsu threshold technique for segmentation of lymphocytes. After extracting shape-based features, k nearest neighbor classifier was trained to achieve an accuracy of 93%.…”

Section: Discussionmentioning

confidence: 99%

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Shafique

Tehsin

2018

Technol Cancer Res Treat

242

138

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Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

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Section: Discussionmentioning

confidence: 99%

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Shafique

Tehsin

2018

Technol Cancer Res Treat

242

138

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“…This algorithm constructs a line between highest histogram value of the image and lowest histogram value through which an optimal threshold value is calculated and image is segmented based on that threshold value [ 45 ]. For acute lymphoblastic leukaemia detection, Otsu's thresholding [ 16 , 21 ] method performs better and provides 93% overall accuracy as compared to Zack's algorithm [ 17 , 29 ] that is able to achieve 92% results.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

Shafique

Tehsin

2018

Computational and Mathematical Methods in Medicine

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Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.

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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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Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour

Cited by 49 publications

References 15 publications

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

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

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