## 3 Results
### Overview of Selected Studies
Specifically, multi-color fusion strategies can be categorized as follows: early fusion, in which color channel combination is performed at the input level; intermediate fusion, in which each color channel is processed separately before merging the extracted features for classification; and finally, late fusion, in which the final predictions from each color channel are combined separately. The models were trained and evaluated using 5-fold cross-validation, with 80% of the data used for training and 20% for validation. Model performance was evaluated in terms of accuracy, specificity, sensitivity, F1 score, and AUC. The results show that all fusion strategies outperformed single-channel approaches. Among them, intermediate fusion showed the highest efficiency, achieving 83.31% accuracy, 0.8523 F1 score, and 0.8999 AUC in step 1. In step 2, its performance improved further, achieving an accuracy of 92.24%, an F1-score of 0.8788, and an AUC of 0.9781. Similarly, for multi-class classification, the intermediate fusion strategy proved to be the most effective, yielding an accuracy of 89.31%, an F1 score of 0.8471, and an AUC of 0.9467. Intermediate fusion had the fewest misclassifications between UM and CN. Among the single-color channels, the red channel showed the highest accuracy (88.12%), F1 score (0.8218), and AUC (0.9069) at step 2 among all the channels tested. The red channel provided the most detailed image of the retinal pigment epithelium and choroid, where UM and CN are usually located. In comparison, the green and blue channels showed inferior performance, indicating the presence of less useful information for the detection of UM and CN [28]. [PERSON] et al. 's study [29] was conducted at the Charite University Hospital in Berlin and included subjects with CN, untreated CM, and irradiated CM. The study collected a total of 762 CFPs of choroidal lesions during the period from 2010 to 2023. Imaging methods used included SD-OCT (spectral-domain optical coherence tomography), FAF, US, and biomicroscopy. The choice of imaging device was based on the location of the choroidal lesion, with CFPs obtained using either the Optos or Charus system. The DL HyperTumorEyeNet model, a CNN based on ResNet50, was run with pre-trained weights using HSA KIT version 1.5.13.10 to classify images into three categories (nevi, untreated melanoma, and irradiated melanoma) and two categories (nevi vs. untreated melanoma). Training and validation of the models were conducted using 90% and 10% of the dataset, respectively, with additional testing performed on an independent test set. Lesion classification was based on the MOLES score, and the models were developed using CNNs, specifically HyperTumorEyeNet based on ResNet50, with comparisons to EfficientNet B4, Vision Transformer, and ConvNextV2. ResNet50 achieved the highest accuracy of 92.65%, outperforming other models. The superior performance of ResNet50 is likely attributed to its pre-trained weights and the relatively small dataset, which makes composite methods less effective. The binary classification (nevi vs. melanoma) achieved an accuracy of 90.9%, with a misclassification rate of 12.1% for malignant lesions. The multi-class classification performed with Optos and Charus achieved a higher accuracy (84.8%) and AUC (0.96) compared to the multi-class classification performed with Charus only. Classification accuracy was highest for irradiated CM (94.0%). The average AUC confirmed the strong performance of the model, with the highest accuracy (95.8%) being achieved in binary classification using Charus images. The MOLES score outperformed the DL classification, incorrectly classifying 12.1% of melanomas as benign. The findings suggest that DL models have the potential to serve as a promising tool for the screening of choroidal tumors, although improvements are needed for borderline cases [29]. [PERSON] et al. [30] developed and validated ML models to predict the transformation of CN to melanoma using multimodal imaging.