| Patel & Mishra (2015) |
2015 |
K-means clustering for detection of WBC. Histogram and Zack algorithm for grouping WBCs, SVM for classification |
Efficiency: 93.57 -% |
ALL-IDB |
7 |
| Chin Neoh et al. (2015) |
2015 |
Multilayer perceptron, Support Vector Machine (SVM) and Dempster Shafer |
Accuracy: Dempster-Shafer method: 96.72% SVM model: 96.67% |
ALL-IDB2 |
180 |
| Negm, Hassan & Kandil (2018) |
2018 |
Panel selection for segmentation, K-means clustering for features extraction, and image refinement. Classification by morphological features of leukemia cells detection |
Accuracy: 99.517% , Sensitivity: 99.348%, Specificity: 99.529% |
Private datasets |
757 |
| Shafique et al. (2019) |
2019 |
Histogram Equalization, Zack Algorithm, Watershed Segmentation, Support Vector Machine (SVM) classification |
Accuracy: 93.70% Sensitivity: 92% Specificity: 91% |
ALL-IDB |
108 |
| Abbasi et al. (2019) |
2019 |
K-means and watershed algorithm, SVM, PCA |
Accuracy, specificity, sensitivity, FNR, precision all are above 97% |
private |
Not mentioned |
| Mishra, Majhi & Sa (2019) |
2019 |
Triangle thresholding, discrete orthogonal S-Stransform (DOST), adaboost algorithm with random forest (ADBRF) classifier |
Accuracy: 99.66% |
ALL-IDB1 |
108 |
| Bhavnani, Jaliya & Joshi (2016) |
2019 |
MI based model, local directional pattern (LDP) chronological sine cosine algorithm (SCA) |
Accuracy: 98.7%, TPR:987%, TNR:98% |
AA-IDB2 |
Not mentioned |
| Abbasi et al. (2019) |
2019 |
K-means and watershed algorithm, SVM, PCA |
Accuracy, specificity, sensitivity, FNR, precision all are above 97% |
Private |
Not mentioned |
| Sukhia et al. (2019) |
2019 |
Expectation maximization algorithm, PCA, sparse representation |
Accuracy, Specificity, Sensitivity all more than 92% |
ALL-IDB2 |
260 |
| Ahmed et al. (2019) |
2019 |
CNN |
Accuracy: 88% leukemia cells and 81% for subtypes classification |
ALL-IDB, ASH Image Bank |
Not mentioned |
| Matek et al. (2019) |
2019 |
ResNeXt CNN |
Accuracy, Sensitivity and precision above 90% |
Private |
18,365 |
| Sahlol, Kollmannsberger & Ewees (2020) |
2020 |
VGGNet, statistically enhanced Salp Swarm Algorithm (SESSA) |
Accuracy: 96% dataset 1 and 87.9% for dataset 2 |
ALL-IDB, C-NMC |
Not mentioned |
