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| Authors (year) | Feature set used | Methods of classification | Parameters used (%) | Dataset used |
|
| Huang and Lai (2010) [15] | Texture features | Support vector machine (SVM) | Accuracy = 92.8 | 1000 × 1000, 4000 × 3000, and 275 × 275 HCC biopsy images |
|
| Di Cataldo et al. (2010) [45] | Texture and morphology | Support vector machine (SVM) | Accuracy = 91.77 | Digitized histology lung cancer IHC tissue images |
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| He et al. (2008) [46] | Shape, morphology, and texture | Artificial neural network (ANN) and SVM | Accuracy = 90.00 | Digitized histology images |
|
| Mookiah et al. (2011) [47] | Texture and morphology | Error backpropagation neural network (BPNN) | Accuracy = 96.43, sensitivity = 92.31, and specificity = 82 | 83 normal and 29 OSF images |
|
| Krishnan et al. (2011) [48] | HOG, LBP, and LTE | LDA | Accuracy = 82 | Normal-83
OSFWD-29 |
|
| Krishnan et al. (2011) [48] | HOG, LBP, and LTE | Support vector machine (SVM) | Accuracy = 88.38 | Histology images Normal-90
OSFWD-42
OSFD-26 |
|
| Caicedo, et al. (2009) [8] | Bag of features | Support vector machine (SVM) | Sensitivity = 92
Specificity = 88 | 2828 histology images |
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| Sinha and Ramkrishan (2003) [17] | Texture and statistical features | NN | Accuracy = 70.6 | Blood cells histology images |
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| The proposed approach | Texture, shape and morphology, HOG, wavelet color, Tamura’s feature, and LTE | KNN | Average: accuracy = 92.19, sensitivity = 94.01, specificity = 81.99, BCR = 88.02, F-measure = 75.94, MCC = 71.74 | 2828 histology images |
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