# High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN
## 3 Technical Challenges in Diagnosing Facial Skin Problems
### Challenge #1: Detecting Small-Sized Skin Problems Such as Pores, Moles, and Acne  
The CNN algorithm can effectively analyze spatial information. CNN is based on the shared-weight architecture of the convolution kernels and filters that slide along input features and provide translation-equivariant responses known as feature maps [38].  
However, the performance of CNN models drops when the size of the target object in an image is considerably small. Some studies have proposed the problems of detecting small-sized objects by CNN models and proceeded experiments to find better algorithms  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Input Image** & **Functionality** & **Enhanced Section** \\ \hline [PERSON] [14] & Ground-Penetrating Radar Image & To detect signals for cracks in asphalt pavement & Applied two feature pyramid networks as backbone networks in Mask R-CNN. \\ \hline [PERSON] [15] & Remote Sensing Images & To detect and segment ships & Added bottom-up structure to the feature pyramid networks in Mask R-CNN. \\ \hline [PERSON] [16] & Captured images by Unmanned aerial vehicle (UAV) & To detect objects in UAV perspective & Modified backbone network in YOLO to increase receptive fields. \\ \hline [PERSON] [17] & Medical Image & To segment liver and liver tumor & Added a residual path with deconvolution and activation operations to the skip connection of the U-Net. \\ \hline [PERSON] [18] & 3D Magnetic Resonance Imaging Image & To segment breast tumor in 3D image & Proposed a segmentation structure consisting of localization and segmentation by two Pseudo-Siamese networks (PSN). \\ \hline [PERSON] [19] & Captured images by UAV & To detect objects in UAV perspective & Proposed a detection model consisting of two detection networks to compute low-dimensional features \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative Studies for Detecting Small Size Objects.  
for detecting the objects [39; 40; 41]. This is due to the limited spatial features exposed in the small-sized object image, and consequently, the limited extraction of spatial features with filters.  
Using our collection of 2225 facial photos (at a resolution of 576 \(\times\) 576 pixels), the average occupation ratios of facial skin problem areas on the photos are measured, as shown in Table 2.  
The whole face occupies an average of 33.06% of the photo. A face section such as eye, nose, mouth, or ear occupies an average of 3.36% of the photo. A facial skin problem, such as pore, mole, or acne, occupies an average of 0.02% of the photo. The average radius of a pore is 0.02-0.05 mm [42], and the average radius of a mole is about 6 mm [43].  
The hypothesis from this observation is that the detection of such small-sized facial skin problems with CNN models results in significantly low performance. To validate our hypothesis, CNN models using the Mask R-CNN algorithm [44] were trained to detect and visually segment 3 different types of objects. The model was trained with ResNet as the backbone, 0.001 for the learning rate, and (16, 32, 64, 128, 256) as the RPN anchor size. The performance of detection results using the Dice Similarity Coefficient (DSC) metric is shown in Table 3.  
The average DSC of a mole is shown to be only 31.5%, which is considerably lower than the average DSC of 95.6% for the entire facial area and the DSC of 90.9% for the mouth area.  
This is due to the size of the mole, which is too small for the filters in a CNN model to detect the spatial characteristics. If a mole is represented with 25 pixels and the size of a filter is (5 \(\times\) 5 (i.e., 25 pixels)), then the visual features of the mole are not captured enough by the filter; rather, the features are even simplified and lost through the process of convolution. The resulting feature map could not represent the mole with sufficient information.  
Moreover, the mask placed around such a small object like a mole by a Mask R-CNN model cannot present its boundary with a high distinction.