# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 3 Experiments and Results
### Experimental Outcomes of Deep Learning Algorithms
In this analysis, the modality-wise F1-score refers to the evaluation across seven modalities present in the training, validation, and testing datasets. The goal was to assess the model's learning performance for each individual modality. Figure 7 shows the confusion matrix of the testing phase. We generated multiple testing subsets, and there was one that we have selected from DRXR that means mostly x-ray images. This confusion matrix is the result of that testing subset. Here in the confusion matrix, as we can see, label CUI2 has a high value assigned to it, and CUI2 stands for X-Ray Computed Tomography as all images belong to this specific modality, so it has high value. This result is obtained by training the prototype on DenseNet121. Figure 6: Example of model predictions on a chest x-ray. Although the global F1-score achieved by the DenseNet121 model remains high, Figure 8 clearly highlights a strong class imbalance. Most of the correct predictions are concentrated in a few dominant concepts (such as CUII and CUI2), while several other classes are underrepresented or not predicted at all. To provide a more balanced evaluation of the model's performance, we also calculated the Macro-F1-score, which was 78% for DenseNet121 and 77% for ResNet101. These values better reflect the impact of class imbalance on model performance. Figure 8 presents the training and validation accuracy and loss curves obtained during the training of DenseNet121 on the ImageCLEF dataset. The graph indicates that reasonable accuracy could not be achieved without applying transfer learning. Increasing the number  
Figure 8: Training and validation accuracy and loss of DenseNet121 without transfer learning and maximizing 20 epochs. Figure 7: Confusion matrix of DenseNet121 prototype. of epochs from 10 to 20 did not produce a significant improvement in the results. This analysis highlights the importance of transfer learning in the context of this study. Figure 9 presents the training and validation accuracy and loss curves obtained during the training of ResNet101 on the ImageCLEF dataset. The graph demonstrates that good accuracy can be achieved by applying transfer learning. This analysis further highlights the importance of transfer learning for this study. In this experiment, the number of epochs was set to 2 to control the increase in time complexity associated with a higher number of epochs. Figure 10 shows the training and validation accuracy and loss graph when VGG19 was trained on the ImageCLEF dataset. This graph shows that maximizing transfer learning can achieve good accuracy but also that this prototype is overfitting. Here we set the number of epochs to 1 because the time complexity also increases by increasing the number of epochs. Figure 10: Training and validation accuracy and loss of VGG19. Figure 9: Training and validation accuracy and loss of ResNet101. Although the models achieved reasonable accuracy, it is important to note that the training processes were limited to a very small number of epochs (2 for ResNet101 and 1 for VGG19) mainly due to time complexity constraints. As a result, the training and validation curves shown in Figures 9 and 10 do not fully represent complete convergence. In future developments, we plan to extend the number of training epochs and adjust the early stopping criteria to allow a more complete model convergence and performance stabilization.