# 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
\begin{table}
\begin{tabular}{l} \hline \hline
**Code.** Implementing a Python Class for training 6 segmentation models \\ \hline
**1: import** tensorflow as tf \\
**2: import** tensorflow.keras as keras \\
**3: import** tensorflow.keras.layers as KL \\
**4: from**.backbone **import**build_mobilenet, build_xception, build_vgg16, build_vgg19, build_resnet50, \\
**5: build_resnet101, build_fusion_deconv** \\
**6: from**.mrcnn **import**build_rpm_and_mrcnn \\
**7:**
8: class FSPSegmenter: \\
**9:** def _init_(self, backbone_type, config, p_model): \\
**10:** self.config = config \\
**11:** self.path_model = p_model \\
**12:** self.model = self.build(backbone_type) \\
**13:** def _build_(self, backbone_type): \\
**14:** input = KL_Input(shape= self.config.img_shape) \\
**16:** \# To build backbone \\
**17:** feature_maps_MobileNetV2 = self.build_mobilenet(input_img) \\
**18:** feature_maps_xception = build_xception(input_img) \\
**19:** feature_maps_vgg16 = build_vgg16(input_img) \\
**20:** feature_maps_vgg19 = build_vgg19(input_img) \\
**21:** feature_maps_resnet50 = build_resnet50(input_img) \\
**22:** feature_maps_resnet101 = build_resnet101(input_img) \\
**23:** feature_maps_fusion_deconv = build_fusion_deconv(input_img) \\
**24:** \\
**25:** \# To initialize structures for region proposal networks and networks for segmentation and detection \\
**26:** self.model_mobileNetV2 = build_rpn_and_mrcnn(feature_maps_MobileNetV2) \\
**27:** self.model_xception = build_rpn_and_mrcnn(feature_maps_xception) \\
**28:** self.model_vgg16 = build_rpn_and_mrcnn(feature_maps_vgg16) \\
**29:** self.model_vgg19 = build_rpn_and_mrcnn(feature_maps_vgg19) \\
**30:** self.model_resnet50 = build_rpn_and_mrcnn(feature_maps_resnet50) \\
**31:** self.model_resnet101 = build_rpn_and_mrcnn(feature_maps_resnet101) \\
**32:** self.model_fusion_deconv = build_rpn_and_mrcnn(feature_maps_fusion_deconv) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Code segment of training segmentation models using the 6 backbone networks. ## 6 Concluding Remarks

The condition of the facial skin is perceived as a vital indicator of the person's apparent age, perceived beauty, and degree of health. For this reason, people wish to maintain youthful facial skin without aging symptoms. Machine-learning-based software analytics on facial skin conditions can be a time- and cost-efficient alternative to the conventional approach of visiting the clinics. However, the current CNN-based approaches have been shown to be limited in the diagnosis performance and, hence, limited in their applicability in clinics. In this paper, the set of 5 technical challenges in diagnosing facial skin problems were addressed. Then, a set of 5 effective design tactics to overcome the technical challenges in diagnosing facial skin problems were presented. Each proposed tactic is devised to resolve one or more technical challenges. Using a data collection of 2225 photos, a total of 30 segmentation models were trained and applied to the experiments. The experiments showed 83.38% of the diagnosis performance when applying all 5 tactics, which outperforms conventional CNN approaches by 32.58%. The diagnosis system presented in this study can potentially be utilized in developing clinical diagnosis systems. Conceptualization, M.K. ; Methodology, M.K. and M.H.S. ; Software, M.K. and M.H.S. ; Investigation, M.H.S. ; Writing--original draft, M.K. and M.H.S. ; Supervision, M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest. ## References

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