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Computational and Mathematical Methods in Medicine
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Computational and Mathematical Methods in Medicine
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2022
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Article
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Tab 9
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Research Article
ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence
Table 9
Comparison of accuracies of CNN+LSTM architectures.
Individual CNN
CNN+LSTM training accuracy in %
CNN+LSTM testing accuracy in %
AlexNet
86.81
77.56
DarkNet-19
83.46
68.51
ResNet-50v2
79.57
83.21
DenseNet-201
87.1
84.7
EfficientNet-B7
67.53
77.56
VGG-16
72.7
71.76
VGG-19
69.46
70.16
NasNetLarge
83.57
78.1
InceptionResNetV2
84.52
80.36
Integrated CNN
CNN+LSTM training accuracy
CNN+LSTM testing accuracy
ADaRDEV
2
I-22
81.43
81.9
ADaRDEV
2
-22
78.56
78.56
RDEV
2
-22
79.54
77.01
ADaDR-22
84.61
82.17
ADaR-22
73.23
70.57
DaRD-22
76.84
79.56
DEV-22
64.82
67.98
ADa-22
62.32
60.19
RD-22
73.1
64.6
RV-22
36.76
41.43
AD-22
78.15
67.83
DaR-22
74.26
72.94