Table 9. Performance evaluation parameters used for RBC classification.

Author	Classification	Performance accuracy %	Evaluation parameter
Du et al. (2019)	CNN model	90.7	Precision, recall and F1 score
Sampathila, Shet & Basu (2018)	GUI	96.7	Accuracy
Kihm et al. (2018)	CNN	85.6 & 91.8	Prediction accuracy
Imran & Ahmad (2017)	SVM and ELM	96	Accuracy
Das, Maiti & Chakraborty (2018)	Random forest	99.42	Accuracy
Yi, Moon & Javidi (2016)	Gabor-filtered holographic	99	Accuracy
Abood, Karam & Hluot (2017)	Fuzzy logic	98	Accuracy
Xu et al. (2017)	Deep CNN	91.01, 89.28	Accuracy, mean evaluation accuracy
Acharya & Kumar (2017)	Modified watershed transform	98	Accuracy