## 4 Results
### Categorization of the Results
#### 4.3.3 Research Methods
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Classification** & & \(N\) = **34** \\ \hline Planar & DenseNet21 & [35] \\  & MobileViT + GO & [58] \\ SPECT & **CNNs** & \\  & Custom CNN & [22, 26, 33, 37, 38, 40, 61], \\  & Custom CNN, VGG16, DenseNet, MobileNet, Inception & [24] \\  & Custom CNN, EfficientNet-B0, MobileNet-V2 & [44] \\  & VGG16 & [39, 52] \\  & VGG16 from scratch and with transfer learning & [54] \\  & Custom feature-fusion VGG19 & [28] \\  & ResNet50V2 & [27] \\  & EfficientNet V2 & [34] \\  & VGG16, VGG19, DenseNet, AlexNet, GoogleNet, NASNet-Large, ResNet, & [25] \\  & VGG16, VGG19, DenseNet, ResNet and custom VGG7, VGG21 and VGG24 & [55] \\  & VGG, Xception, MobileNet, EfficientNet, Inception, DenseNet, ResNet \\  & VGG16, LDA, SVN, DT, MLP, RF, AB & [47] \\  & VGG16, AlexNet + Multi-kernel SVM & [49] \\  & PD Net (previously published model) & [48] \\  & DETR (previously published model) & [62] \\
**Other Deep Learning** & & \\  & AE + AB, SVM, KNN, RF, GB, BC, MLP, DT, LR & [51] \\  & Stacked AE & [42] \\  & FNN, DT, RF, LR, KNN, SVM, LDA & [43] \\  & **Other Machine Learning** & \\  & DM + LDA & \\  & Density-Based Spatial, K-means and Hierarchical Clustering & [46] \\  & SVM & [45] \\  & SVM, KNN, DT, BT, RF & [31] \\  & SVM, DT, RF, LR, MLP, GB, XGB & [30] \\  & SVM, KNN, DT, RF, LR, NB, AB, GB, SGD & [50] \\  & SVM, KNN, DT, RF, LR, MLP, NB, GB, XGB & [29] \\ \hline
**Segmentation** & & \(N\) = **10** \\ \hline Planar & FNN & [64] \\  & Custom Model (Swin-Unet + DeepLab) & [59] \\  & Custom CNN & [57] \\  & U-net & [20, 23, 36, 53, 63] \\  & V-net + dynamic programming & [21] \\  & U-net, Mask R-CNN & [56] \\ \hline
**Classification and Segmentation** & & \(N\) = **1** \\ \hline Planar & Mask R-CNN & [60] \\ SPECT & - & \\ \hline  & \multicolumn{3}{c}{“+” _denotes combination of methods_, “_” _denotes comparing of methods_, “_” _denotes base models_. CNN—convolution neural network, LDA—linear discriminant analysis, GAN—generative adversarial network, SVM—support vector machines, NN—neural network, DT—decision tree, MLP—multilayer perceptron, RF—random forest, AB—adaptive boosting, DM—diffusion maps, AE—autoencoders, FNN—feed-forward NN, LR—logistic Regression, KNN—K-nearest neighbors, XGB—Extreme Gradient Boosting, GB—Gradient Boosting, BT—Boosted Tree, GO—Growth Optimizer, NB—Naive Bayes, SGD—Stochastic Gradient Descent, BC—Bagging Classifier. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research tasks and methods: **diagnostic methods** (\(N\) = 45). vector machine (SVM), decision tree (DT), and random forest (RF) are used when numerical features serve as input. These features may include calculated values like specific binding ratio (SBR) [42; 43; 45; 48; 50], handcrafted filter-based or statistical features [31], or flattened image data transformed into one-dimensional vectors, often with dimensionality reduction techniques such as principal component analysis (PCA) [47]. Performance metrics used in classification tasks include accuracy, sensitivity, specificity, recall, precision, F1-score, AUC, and ROC. **Segmentation** is less commonly studied compared to classification. For segmentation tasks, U-Net is the preferred model, though some studies employ V-Net, Mask R-CNN, or custom architectures. For example, [PERSON] et al. [60] integrated segmentation and classification by using Mask R-CNN outputs as input to a fully convolutional network or a set of fully connected layers for each task. Performance metrics for segmentation include the dice coefficient, precision, recall, Hausdorff distance, class pixel accuracy, and cross-merge ratio (MIoU). Considering that **general imaging methods** are applied to different tasks, variations in technology selection can be observed. Deep learning models, such as CNN, GAN, and U-Net, are predominantly used both independently and as a foundation for the development of new models.