Table 3 This table gives a summary of recent work that has been executed in prostate cancer detection using machine learning and deep learning algorithms as discussed in Sect. 2.3
References | Feature extraction | Data | ML/DL | Acc, AUC, or ROC (%) |
|---|---|---|---|---|
[69] 2018 | 3-D CNN | images from CEUS videos | 3-D CNN, J48, logistic, RF, | Â |
| Â | Â | Â | Decision Table, FLDA, KNN | 90 |
[70] 2018 | level set-based approach, GGMRF | DWI images | SNCSAE, RF, Random Tree, | 94 |
[71] 2019 | normalization and scaling | NCI PLCO | KNN, SVM, DT, RF, MLP, | Â |
| Â | Â | Â | Adaptive boosting, Quadratic discriminant analysis | 91 |
[72] 2019 | modified ResNet, DT | DWI images | RF | 87 |
[73] 2020 | patch extraction principle | NASNetLarge | 97.3–98 | |
[64] 2020 | MRI images | GoogleNet, Bayes, decision tree, | Â | |
| Â | Â | Â | SVM Gaussian, SVM RBF, SVM polynomial | 100 |
[68] 2021 | Statistical methods | MRI images | Kernel Naïve Bayes, DTs, SVM-Gaussian, |  |
| Â | Â | Â | KNN-Cosine, LSTM, RUSBoost Tree | 100 |
[76] 2021 | 3-D U-Net | bpMRI images | U-Net | 85 |
[67] 2022 | VGG16 | RF, SVM, Gradient boosting, NN, | Â | |
| Â | Â | Â | MobileNetV2, ResNet50V2, Resnet101V2, | Â |
| Â | Â | Â | Resnet152V2, Xception, VGG16, VGG19, | Â |
|  |  |  | InceptionResNetV2, and InceptionV3 | 88–97 |
[80] 2022 | slide tiling, Otsu’s method [81] | whole slide images, TCGA data [74] | EfficientNetB1 | 98–99 |