# High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN
## 4 Design of Tactics for Remedying the Technical Challenges
### Design of the Main Control Flow
For the facial photos with acne problems, the conventional Mask R-CNN model yielded 56.63% of DSC where the Enhanced Mask R-CNN model yielded 73.26% as shown in the figure. A significant degree of performance has been gained. As the summary of the experiment, the performances of segmenting acne, age spots, and mole problems were increased by 16.63%, 13.7%, and 19.05%, respectively, and the average of performance gains for all 3 types of facial skin problems is 16.46%. #### 5.4.2 Experiment for Tactic #2: Super Resolution GAN Model

This experiment is to evaluate the effectiveness of applying Super Resolution GAN model in enhancing the quality of facial images. A dataset of 3 facial skin problem types was made: acne, rosacea, and wrinkle. A segmentation model was trained with both the Mask R-CNN and the SR-GAN structure and performed the experiments. The results of applying the super resolution tactic with SR-GAN are shown in Figure 14.

In the figure, the original face image is partitioned into 9 images of (192 \(\times\) 192) size, which is required by the trained Mask R-CNN model. Then, each image is fed into the SR-GAN model that will enhance the quality of images as shown in the figure. To compare the performance of super resolution, a segmentation model was trained with conventional Mask R-CNN and another segmentation model with both Mask R-CNN and SR-GAN. The comparison of the performances of the two models is shown in Figure 15.

Figure 14: Result of applying super resolution with SR-GAN. Figure 13: Comparing performances of conventional Mask R-CNN and enhanced Mask R-CNN models. The Mask R-CNN segmentation model was applied to detect all the face photos, the performance was measured in DSC, and the average of all the DSC measurements was computed. Then, a segmentation model with both Mask R-CNN and SR-GAN was trained and applied to perform the same operations. For wrinkle problems, the DSC with the Mask R-CNN model yielded 49.97% of DSC where the Enhanced Mask R-CNN model yielded 67.24% of DSC, as shown in the figure. A significant degree of performance has been gained. As the summary of the experiment, the performances of segmenting acne, rosacea, and wrinkle problems were increased by 8.63%, 12.46%, and 17.27%, respectively, and the average of performance gains for all 3 types of facial skin problems is 12.79%. #### 5.4.3 Experiment for Tactic #3: Facial Skin Problem-Specific Models

This experiment is to evaluate the effectiveness of applying facial skin problem-specific models instead of using a single integrated model. A dataset of face photos for 5 types of skin problems was used: acne, age spots, moles, rosacea, and wrinkles. An integrated Mask R-CNN model for all 5 types of facial skin problem types was trained. Then, a set of 5 individual segmentation models for 5 different facial skin problem types were trained. Then, their performances were measured and compared as shown in Figure 16.

Figure 11:0 Comparing performance of integrated model and facial skin problem type-specific model. Figure 11:1 Comparing performances of segmentation with and without SR-GAN. For wrinkle problems, the integrated segmentation model yielded 45.97% of DSC where the wrinkle-specific segmentation model yielded 59.74% of DSC, as shown in the figure. A significant degree of performance has been gained. The performances of segmenting acne, age spots, moles, rosacea, and wrinkles were increased by 8.2%, 11.06%, 9.07%, 8.6%, and 13.77%, respectively, and the average of performance gains for all 5 types of facial skin problems is 10.14%. #### 5.4.4 Experiment for Tactic #4: Face Direction-Specific Models

This experiment is to evaluate the effectiveness of applying face direction-specific models instead of using a single integrated model. A dataset of face photos taken in 3 different directions was used in this experiment. A Mask R-CNN model was trained to segment face photos in any direction. Then, a set of 3 individual models for 3 face directions were trained: left-side, frontal, and right-side directions.