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