# _Applied Sciences_
## 2 Methods and Materials
### Utility Level Evaluation  
As the methods described above rely on reducing, to a certain degree, the information from the original images, their utility after the obfuscation must be evaluated. To perform this analysis, the same DL model is trained for multiple levels of obfuscation, including no obfuscation. The methods presented in Sections 2.1 and 2.2 are employed separately and in conjunction, as described in Section 2.3, to obfuscate an in-house dataset consisting of coronary angiography frames. The same experiment is run for multiple values of \(N\), ranging between 1 and 255. The utility of obfuscated images is determined by comparing the accuracy achieved on a testing dataset for different degrees of obfuscation.  
The task is to train a binary classifier to distinguish between RCA and LCA views in angiographic frames. Figure 7 depicts one sample of each category. The dataset contains 3280 coronary angiographies, balanced between the two classes. A subset of 600 images is used for validation, and another subset of 700 images is retained for evaluation purposes. The rest of the 1980 angiographic frames are used for training. Augmentation techniques such as shifting, flipping, zooming and rotation are applied. The original size of the frames is 512 \(\times\) 512 pixels, but experiments with different input shapes have shown that a size of 128 \(\times\) 128 ensures almost no loss in classification performance with a lower computational time. The pixels values are normalized through min-max scaling in the [0, 1] range.  
Figure 5: Schematic representation of the secure obfuscation algorithm.  
Figure 6: Comparison between an original angiographic frame (**a**) and the obfuscated counterparts when using (**b**) encoding, (**c**) non-bijective intensity mapping, and (**d**) combined algorithm.  
The images (obfuscated or not) are passed through four convolutional layers of 16 and 32 filters with a 3 \(\times\) 3 receptive field during training. The pooling layers downsample the images by a factor of two by using the maximum value of a window. After the last convolutional layer, a flatting layer is added to convert the features matrix into a vector. The fully connected layers contain 512, 1024 and 2 nodes, respectively. The ReLU function is employed as an activation function for all layers, except for the last one where the softmax activation is used. Each convolutional layer is followed by a local normalization layer [30] to make the model more robust to image degradation. To limit the overfitting, between 25% and 50% of the connections of the neurons are dropped through dropout layers. Furthermore, although the maximum number of epochs is set to 30, early stopping is employed when the validation loss is not decreasing within 10 consecutive epochs. A learning rate scheduler is used to achieve good convergence, starting from 1 \(\times\) 10\({}^{-3}\), and diminishing the value with every epoch. The workflow of an inference step using the obfuscation algorithm is depicted in Figure 8.  
The Keras framework [31] was used to build the convolutional neural network, and the local normalization layer is based on [30]. The experiments were run on a computer equipped with an Intel i7 CPU (Intel, Santa Clara, CA, USA) at 4.2 GHz, 32 GB RAM  
Figure 8: Detailed workflow of inference using the secure obfuscation algorithm.  
Figure 7: RCA—right coronary artery (**a**) and LCA—left coronary artery (**b**).  
and an NVIDIA GeForce GTX 1050 Ti GPU (Nvidia, Santa Clara, CA, USA) with 4 GB of dedicated memory.