# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 3 Experiments and Results
### Experimental Outcomes of Deep Learning Algorithms  
Table 11 presents the experimental results obtained from different deep learning models under various settings. The ImageCLEF dataset, consisting of 3047 concepts, was used throughout the experiments. The initial experiments involved training a DenseNet121 model with transfer learning, utilizing ImageNet pre-trained weights and setting the number of epochs to 10. Training on a CPU-based system required approximately 15 days and achieved an F1-score of 0.33. Subsequently, DenseNet121 was trained without transfer learning under the same epoch setting on a GPU-based system, which took approximately one month to complete and yielded an F1-score of 0.21. Experiments were also conducted with a ResNet101 model using transfer learning (ImageNet pre-trained weights) and three epochs. Training on a CPU-based system required about seven days, resulting in an F1-score of 0.21. When ResNet101 was trained without transfer learning on a GPU-based system (three epochs), training lasted about 20 days, and the F1-score obtained was 0.17. Finally, the VGG19 model was trained on the dataset without transfer learning, achieving an F1-score of 0.265. Based on these results, DenseNet121 with transfer learning was selected as the optimal approach, as models with fewer layers or without transfer learning consistently yielded lower performance. Table 12 shows the experimental outcomes of different profound learning versions with alternative settings. We initially used the ImageCLEF dataset of 3047 concepts. However, we did not obtain any meaningful results, so we reduced the number of concepts to 20. We started with training the base prototype of DenseNet121 with the transfer learning concepts, i.e., weights of ImageNet pre-trained on DenseNet121 and settings epochs equal to 3. It took almost three days to train the prototype on a CPU-founded system. This gave us an F1-score of 0.89. To better illustrate the model's behavior in predicting medical concepts from radiological images, we provide an example in Figure 6. The ground truth labels for the image are also shown for comparison. As can be seen, the model correctly identifies the major anatomical structures (\"Chest X-ray\", \"Lung\", \"Effusion\") with high confidence, and additionally predicts the concept \"Opacity\", which was not annotated in the ground truth but is a plausible finding in chest radiographs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prototype** & **External** & **F1-Score** \\ \hline DenseNet121 & ImageNet & 0.33 \\ ResNet101 & ImageNet & 0.21 \\ VGG19 & None & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Models comparison using 3047 concepts. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prototype** & **External** & **F1-Score** \\ \hline DenseNet121 & ImageNet & 0.89 \\ ResNet101 & ImageNet & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Models comparison using 20 concepts. This significant improvement in classification performance when focusing on the 20 most frequent concepts is mainly attributed to the higher frequency of these classes, which alleviates the few-shot learning problem. Although some label noise may be present in the dataset, it was not identified as the primary limitation in this study. The main challenge encountered was the scarcity of examples for many rare classes. The ResNet101 model was subsequently trained using transfer learning, employing ImageNet pre-trained weights and setting the number of epochs to 3. Training on a CPU-based system required approximately three days and resulted in an F1-score of 0.88. The modality-wise F1-score of the DenseNet121 model was calculated, starting with the DRAN images corresponding to the angiogram modality. However, no meaningful results were obtained. Due to time constraints, this process was discontinued, as generating new CSV files proved to be complex and time-consuming.