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Table 5 Accuracy of the trained Resnet-101 model in classifying ALL in microscopic images when the algorithm hyperparameter combinations in Table 4 were used in three independent experimental runs

From: Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method

Experiments 1–9 Dataset Runs of experiment Average accuracy SD η value
1 2 3
1 Training set 0.9777 0.9796 0.9792 0.9788 0.0010 33.4870
Preliminary test set 0.8045 0.8066 0.7927 0.8013 0.0075 14.0346
2 Training set 0.985 0.9864 0.9872 0.9862 0.0011 37.2024
Preliminary test set 0.7916 0.7943 0.805 0.7970 0.0071 13.8487
3 Training set 0.9211 0.9218 0.9216 0.9215 0.0004 22.1026
Preliminary test set 0.7477 0.7483 0.7483 0.7481 0.0003 11.9754
4 Training set 0.7892 0.7888 0.7893 0.7891 0.0003 13.5185
Preliminary test set 0.6508 0.6508 0.6508 0.6508 0.0000 9.1385
5 Training set 0.9533 0.9538 0.9535 0.9535 0.0003 26.6572
Preliminary test set 0.7783 0.7809 0.7788 0.7793 0.0014 13.1253
6 Training set 0.864 0.8639 0.8647 0.8642 0.0004 17.3420
Preliminary test set 0.6909 0.6904 0.6888 0.6900 0.0011 10.1737
7 Training set 0.985 0.9877 0.985 0.9859 0.0016 37.0156
Preliminary test set 0.8056 0.8013 0.7965 0.8011 0.0046 14.0288
8 Training set 0.9056 0.9057 0.9064 0.9059 0.0004 20.5282
Preliminary test set 0.7268 0.7327 0.7338 0.7311 0.0038 11.4082
9 Training set 0.9796 0.9831 0.9831 0.9819 0.0020 34.8624
Preliminary test set 0.7954 0.7868 0.7563 0.7795 0.0205 13.1318