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| Authors, year | Method | Dataset | Preprocessing | Segmentation | Features extraction | Classification | Performance% |
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| Mishra et al., 2017 | Gray level cooccurrence matrix and random forest based acute lymphoblastic leukaemia detection [13] | Public ALL-IDB 1 (108 images) | Histogram equalization, Weiner filtering | Sobel, Perwitt, Marker based watershed segmentation | GLCM (gray level cooccurrence matrix), PPCA (Probabilistic Principal Component Analysis) | RF (random forest) | Accuracy 96.29% |
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| Karthikeyan and Poornima, 2017 | Microscopic image segmentation using fuzzy -means for leukaemia diagnosis [14] | Google (19 images) | Histogram equalization, Median filter | -means clustering, fuzzy -means clustering algorithm | Gabor texture extraction method | SVM (support vector machine) | Accuracy 90% |
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| Rawat et al., 2017 | Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers [15] | Public ALL-IDB 2 (260 images) | Histogram equalization, Order statistic filter | Global thresholding, morphological opening | Geometrical features, chromatic features, statistical texture features | Hybrid hierarchical classifiers kNN, PNN, SVM, SSVM, and ANFIS | Accuracy 99.2% |
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| Joshi et al., 2013 | White blood cells segmentation and classification to detect acute leukaemia [16] | Public ALL-IDB 1 (108 images) | Contrast Stretching and Histogram equalization | Otsu’s threshold method | Shape features (area, perimeter, and circularity) | KNN (-nearest neighbor) | Accuracy 93% |
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| Putzu and Ruberto, 2013 | White blood cells identification and classification from leukaemia blood image [17] | Public ALL-IDB 1 (108 images) | Histogram equalization | Triangle threshold method using Zack algorithm | Shape based features (area, perimeter, etc.), GLCM features | SVM (support vector machine) | Accuracy 92% |
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| Li et al., 2016 | Segmentation of white blood cell from acute lymphoblastic leukaemia images using dual-threshold method [18] | Public ALL-IDB (130 images) | Global Contrast Stretching | Dual-threshold segmentation | Binarization, morphological erosion, median filtering (postprocessing) | Not mentioned | Accuracy 98% |
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| Amin et al., 2015 | Recognition of acute lymphoblastic leukaemia cells in microscopic images using -means clustering and support vector machine classifier [19] | Isfahan Al-Zahra and Omid Hospital pathology laboratories (146 ALL images and 166 lymphocytes images) | Histogram equalization | -means clustering | Shape based features (area, perimeter, solidity, and eccentricity), histogram-based features (mean, standard deviation skewness, entropy, etc.) | SVM (support vector machine) | Accuracy 95.6% |
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| Savita Dumyan, 2017 | An enhanced technique for lymphoblastic cancer detection using artificial neural network [20] | Blood sample images (36 images) | Histogram equalization | Image binarization, canny edge detection technique | Shape based features, texture features, statistical features, moment invariants | Artificial neural network (ANN) | Accuracy 97.8% |
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| Chatap and Shibu, 2014 | Analysis of blood samples for counting leukaemia cells using support vector machine and nearest neighbour [21] | Public ALL-IDB 1 (108 images) and ALL-IDB 2 (260 images) | Histogram Equalization, Contrast Stretching | Otsu’s threshold method | Shape based features (area, perimeter, and circularity) | -nearest neighbor (KNN) | Accuracy 93% |
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| Amin et al., 2015 | Recognition of acute lymphoblastic leukaemia cells in microscopic images using -means clustering and support vector machine classifier [22] | Isfahan Al-Zahra and Omid Hospital pathology laboratories (312 images) | Histogram Equalization, Linear Contrast Stretching | -means clustering | Geometric or shape based (area, perimeter, convex, and solidity), first- and second-order statistical features | SVM (support vector machine) | Accuracy 97% (blast and normal), 95.6% (subtypes classification) |
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| Patel and Mishra, 2015 | Automated leukaemia detection using microscopic images [23] | Not mentioned | Median Filtering, Wiener Filtering | -means clustering | Color features, geometric, texture, and statistical features | SVM (support vector machine) | Accuracy 93.57% |
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| MoradiAmin et al., 2016 | Computer aided detection and classification of acute lymphoblastic leukaemia cell subtypes based on microscopic image analysis [24] | Isfahan Al-Zahra and Omid Hospital pathology laboratories (312 images) | Histogram equalization | Fuzzy -means, watershed algorithm | Geometric or shape based (area, perimeter, convex, and solidity), first- and second-order statistical features | SVM (support vector machine) | Accuracy 97.52% |
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| Mohapatra and Patra, 2010 | Automated cell nucleus segmentation and acute leukaemia detection in blood microscopic images [25] | University of Virginia, Ispat General Hospital, Rourkela, Odisha (108 images) | Selective median filtering, Unsharp Masking | -means clustering, nearest neighbor | Fractal dimension, shape features including contour signature and texture, color features | SVM (support vector machine) | Accuracy 95% |
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| Mohapatra et al., 2011 | Fuzzy based blood image segmentation for automated leukaemia detection [26] | University of Virginia, Ispat General Hospital, Rourkela, Odisha (108 images) | Selective median filtering, Unsharp Masking | Gustafson Kessel clustering, nearest neighbor | Fractal dimension, shape features including contour signature and texture, color features | SVM (support vector machine) | Accuracy 93% |
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| Mohapatra et al., 2014 | An ensemble classifier system for early diagnosis of acute lymphoblastic leukaemia in blood microscopic images [27] | Ispat General Hospital, Rourkela, Odisha (150 images) | Contrast enhancement, Selective median filtering | Shadowed -means (SCM) clustering | Fractal dimension, shape features including contour signature and texture, color features | Ensemble method (Naive Bayesian, -nearest neighbor, multilayer perceptron, radial basis functional neural network, support vector machines) | Accuracy 94.73% |
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| Samadzadehaghdam et al., 2015 | Enhanced recognition of acute lymphoblastic leukaemia cells in microscopic images based on feature reduction using principle component analysis [28] | Isfahan Al-Zahra and Omid Hospital pathology laboratories (21 Images) | Histogram equalization | Fuzzy -means, watershed algorithm | Geometric or shape based (area, perimeter, convex, and solidity), statistical features | SVM (support vector machine) | Accuracy 96.33% |
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| Putzua et al., 2017 | Leucocyte classification for leukaemia detection using image processing techniques [29] | Public ALL-IDB 1 (108 images) and ALL-IDB 2 (260 images) | Histogram equalization and contrast stretching | Zack algorithm | Shape features, color features, texture features | SVM (support vector machine) | Accuracy 92% |
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| Sadeghian et al., 2009 | A framework for white blood cell segmentation in microscopic blood images using digital image processing [30] | L2 type ALL blood images (20 images) | Gaussian filter, Standard deviation | Canny edge detection technique, Zack algorithm | Not mentioned | Not mentioned | Accuracy 92% (nucleus segmentation), 78% (cytoplasm segmentation) |
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| Mohapatra and Patra, 2010 | Automated leukaemia detection using Hausdorff dimension in blood microscopic images [31] | University of Virginia, Ispat General Hospital, Rourkela, Odisha (108 images) | Selective median filtering, Unsharp masking | -means clustering | Hausdorff dimension, shape features, color features | SVM (support vector machine) | Accuracy 95% |
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| Mohapatra et al., 2010 | Image analysis of blood microscopic images for acute leukaemia detection [32] | University of Virginia, Ispat General Hospital, Rourkela, Odisha (108 images) | Selective median filtering, Unsharp masking | Fuzzy -means clustering, nearest neighbor | Fractal features (Hausdorff dimension), shape features, contour signature, color, texture features | SVM (support vector machine) | Accuracy 95% |
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