# 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
Then, their performances were measured and compared, as shown in Figure 11:2

For the photos of frontal face, the integrated segmentation model yielded 53.12% of DSC where the frontal direction-specific model yielded 65.38%, as shown in the figure. A significant degree of performance has been gained. The average performances of segmenting face photos showing the left side face, frontal face, and right side face were increased by 8.45%, 12.26%, and 8.66%, respectively, and the average of performance gains for all 3 face directions is 9.79%

#### 5.4.5 Experiment for Tactic #5: Discarding False Segmentations

This experiment is to evaluate the effectiveness of discarding false segmentations made on non-facial areas. A Mask R-CNN model was trained and applied to detect facial skin problems. Then, the software component implementing the tactic of discarding false segmentation was implemented and applied to discard any resulting false segmentations. The performance of the diagnosis without applying this tactic and the performance of the diagnosis by applying this tactic were measured and compared as shown in Figure 11:3

For the wrinkle problems, the segmentation with the Mask R-CNN yielded 45.97% of DSC where the performance measure after discarding false segmentations was 61.23%, 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 5.49%, 9.17%, 8.01%, 6.65%, and 15.26%, respectively, and the average of performance gains for all 5 types of facial skin problems is 8.92%. Figure 11:4 Comparing performances of integrated model and face direction-specific models. #### 5.4.6 Experiment for Integrating all 5 Tactics

This experiment is to evaluate the performance of facial skin problem diagnosis by integrating all 5 tactics. A conventional Mask R-CNN segmentation model was trained for all the skin problem types. Then, a total of 30 individual segmentation models were trained for the 5 different types of tactics and the 3 facial photo directions. Then, the performances of 3 different approaches were measured: (1) performance of the conventional Mask R-CNN model, (2) the average performances of 5 different tactics, and (3) the average performance of applying all 30 segmentation models. The performances of the three approaches are compared in Figure 11:5

The conventional Mask R-CNN yielded 50.8% of DSC, the tactic-specific approaches yielded (64.6%, 69.33%, 60.94%, 59.74%, and 59.72%) of DSC, and the approach of integrating all 5 tactics yielded 83.38% of DSC. The integrated approach outperformed the conventional Mask R-CNN approach by 32.58%, and it outperformed the tactic-specific approach by an average of 22.47%. #### 5.4.7 Experiment for Comparing with Other Backbone Networks

This experiment was to compare the performance of our proposed approach with diagnosis models trained with 6 different backbone networks: MobileNetV2, Xception, VGG16, VGG19, ResNet50, and ResNet101. Figure 11:6 Comparing performances of the three approaches. Figure 11:7 Comparing performances of segmentations with- and without discarding false segmentation. Table 5 shows the code segments of training the segmentation models using the 6 different backbone networks. The code segment is to configure (6 + 1) segmentation backbone structures inside of the _build_ method (lines 17 to 23) and to configure the remaining network structures of Mask R-CNN inside in _build_rpm_and_mrcnn_ methods (lines 26-32). Once the network structures are configured, then all 7 segmentation models are trained and applied to detecting facial skin problems using the test set of 445 photos. The comparison of their average performances is shown in Figure 11:8

As shown in the figure, each of the segmentation models with MobileNetV2, Xception, VGG16, VGG19, ResNet50, and ResNet101 shows an average 46.91% of detection performance. It is 17.89% lower than the performance of our proposed model.