## 4 Experiments and Results
### Hydra Heads Performance
#### 4.2.1 Abnormality Classification  
We define the abnormality classification task as categorizing CXR images into normal or abnormal. The abnormal class contains CXR images with different types of pulmonary opacities caused due to TB or lung nodules. Deep learning approaches have been widely used for lung abnormality classification. Figure 3: Examples of reconstructed SR images. First column: original HR images. Second column: downsampled input LR images. Third column: reconstructed images obtained using the proposed SISR model (_Hydra_ Trunk). For example, [PERSON] et al. [30] presented a patch-based multi-resolution CNNs for CXR lung nodule classification. Prior to deep feature extraction, the CXR images are enhanced using a histogram-based method. After enhancing the images, three multi-resolution patches are extracted from each lung field and used to train three CNNs at different resolutions. The final classification label is then generated by fusing the outputs of the three CNNs. The proposed method achieved 0.98 Free-Response Receiver Operating Characteristic (FROC) curve when evaluated on the JSRT dataset. In case of CT images, [PERSON] et al. [39] proposed a GAN-based network, called 3D MCGAN, to generate synthetic and diverse nodules in lung CT scan images at a desired location. The generated synthetic images are then used by 3D Faster RCNN as additional training data for lung nodule detection. The experimental results suggest that the proposed method can achieve higher sensitivity and overcome the medical data paucity. Contrary to these works, we propose to simultaneously learn pre-processing operation and CXR abnormality classification using a single trunk and a task-specific head. To create a task-specific head for abnormality classification, we initialized the SISR model with its weights, freezed the weights, and appended task-specific layers to the truncated SISR model (at the deepest or 6 th convolutional layer). The task-specific layers are global average pooling (GAP), fully connected (FC), dropout (D), and Softmax (SM) layers. We then trained task-specific layers on the Shenzhen dataset to minimize the categorical cross-entropy (CCE) loss. We trained the classification layers using Adam optimizer, \(1\times 10^{-3}\) learning rate, 32 epochs, and a batch size of 8. As a baseline, we used a model that has six convolutional layers followed by GAP, FC, D, and SM layers. The baseline classification model has the following architecture: input (\(256\times 256\)) \(\rightarrow\) Conv2D (\(16\)) \(\rightarrow\) MaxPool \(\rightarrow\) Conv2D (\(32\)) \(\rightarrow\) MaxPool \(\rightarrow\) Conv2D (\(64\)) \(\rightarrow\) MaxPool \(\rightarrow\) Conv2D (\(128\)) \(\rightarrow\) MaxPool \(\rightarrow\) Conv2D (\(256\)) \(\rightarrow\) MaxPool \(\rightarrow\) Conv2D (\(512\)) \(\rightarrow\) GAP \(\rightarrow\) FC \(\rightarrow\) D \(\rightarrow\) SM. We randomly initialized the model's weights and optimized for the search parameters. We trained the baseline classification model to minimize the CCE loss. We performed two baseline experiments using the baseline classification model. In the first experiment, we used the original HR images as input to the classification model. In the second experiment, we applied bi-cubic interpolation to generate interpolated images and used the generated interpolated images as input to the baseline classification model. Recall that the main difference between the proposed approach and the traditional approach is that our approach directly uses the SISR model and only learns the task-specific classification layers. Table 2 presents the performance of abnormality classification using the baseline approach and the proposed approach (_Hydra_ classification head). We trained the head and the baseline model on the Shenzhen dataset, and evaluate their performance on the Montgomery dataset. The performance was reported using the accuracy, AUC, sensitivity, precision, and F-score. As shown in the table, the _Hydra_ outperformed baseline models in all scales.