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. 2021 Apr 7;21(8):2586. doi: 10.3390/s21082586

Table 5.

Summary of publications focused on applying deep learning methods for prostate histopathology images.

Method Reference Year Accuracy Result Software
CNN [31] 2016 AUC ranges from 0.88 to 0.99. N/A
CNN built upon VGG19 [27] 2019 Average accuracy of classifying Artefacts vs. Glands is 95.4%, average accuracy of classifying Benign vs. Pathological is 88.3%, Average accuracy of Multi-class classification is 87.6% Matlab 2018b + Python 3.5 with Keras library and Tensorflow as backend.
Pretrained CNN [30] 2016 The classification accuracy per image patch is 81%, while for the whole images, the classification accuracy is 89%. N/A
Different CNN Architectures ResNet-50 [28] 2018 They evaluated their results using test cohort and they observed that MobileNet attained the best performance on the validation set Python 3 with Keras library and tensorflow as backend. Some analysis was done in R by the help of using survminer and survival packages.
MobileNet
Inception-V3
DenseNet-121
VGG-16
U-Net [29] 2020 The developed model achieved accuracy of 99% for biopsies containing tumor and a specificity of 82%. Tensorflow and Keras
SSA-RideNN [23] 2019 The technique achieved maximal accuracy of 89.6% and sensitivity of 89.1%, and specificity of 85.9% Matlab
SVM [34] 2018 They used Cohen’s kappa coefficient to evaluate the performance. The highest value attained is 0.52 by logistic regression, while 0.37 is attained by using CNN. Matlab
Random forest
linear discriminant analysis
logistic regression
CNN
Different CNN Architectures EfficientNet [113] 2020 UNet attained the best result of AUC about 0.98 N/A
DenseNet
U-Net
cGAN [25] 2018 The proposed technique achieved F1-score 85.7% for prostate dataset Pytorch 0.4
NB that utilizes CNN [26] 2019 Their proposed model achieves 81.3% precision, 91.4% in recall, and 85.4% in F1. Python 2.7 with Keras library and Tensorflow
Path RCNN [24] 2019 Path RCNN attained accuracy of 99% and a mean of area under the curve of 0.99. Python and Tensorflow backend