## 3 Methodology
### Wide-ShuffleNet  
We provide a brief analysis of ShuffleNet inventing for portable devices. Several terms are reviewed, including efficient model designs, group convolution, channel shuffle for group convolutions, and ShuffleNet unit. We also introduce a new variant of ShuffleNet, called wide-ShuffleNet, that has been developed for skin classification. Efficient model designs: Recently, efficient model designs played an essential role in achieving DL networks in many computer vision tasks [59; 60; 61]. The growing demand for streaming high-quality DL architectures on embedded systems boosts the research of effective model designs [62]. For instance, instead of assembling convolution layers, GoogLeNet [63] expands the network depth with sufficient lower complexity. ResNet in [64; 65] achieves remarkable performance using the effective bottleneck architecture. SqueezeNet [66] preserves accuracy; however, it decreases computation and parameters significantly. Group convolution: AlexNet [59] is the first model using the idea of group convolution and spreading the network across two GPUs, and ResNeXt [67] confirms the efficacy of  
Figure 2: Segmentation results of various methods: Otsu, EWS, and EW-FCM. group convolution. MobileNet [68] applies the depthwise separable convolution (DWConv) and achieves the best results between lightweight networks. ShuffleNet performs DWConv and group convolution (GConv) in a new style. Channel shuffle operation: In the early studies on efficient network layout, the operation of channel shuffle is seldom noticed. Lately, another study [69] applied this concept for a two-stage convolution. However, the study [69] did not examine the efficacy of channel shuffle and its application in light network layout. Channel shuffle for group convolutions: New DL architectures [63; 64; 65] consist of duplicated structure blocks with identical designs. Among these architectures, ResNeXt [67] and Xception [70] offer an effective GConv or DWConv toward the structure blocks to discover an outstanding trade-off among computational cost and representation capacity. However, both architectures do not use pointwise convolutions [68] (or one-by-one convolutions), which require significant complexity. Increasing the number of one-by-one convolutions, we must restrict the number of channels to meet the complexity constraint in a small network. One possible approach is using the channel links with GConv on the one-by-one layers to handle the limitation. GConv remarkably decreases computation cost by assuring that individual convolution runs on the relative input channel group. ShuffleNet unit was invented for the light model, benefiting from channel shuffle functioning. ShuffleNet unit gets the ideal from bottleneck unit [64] (see Figure 2a in reference [61]). Building a ShuffleNet unit, the first one-by-one layer in the bottleneck unit is replaced by pointwise GConv accompanied by a channel shuffle functioning (see Figure 2b in reference [61]). The second pointwise GConv retrieves the dimension of the channel to match with the shortcut pathway. There are two types of ShuffleNet units: nonstride and stride. Two modifications are made to create ShuffleNet with stride (see Figure 2c in reference [61]). First, a three-by-three average pooling is added to the shortcut path. Then, the elementwise addition is altered by channel concatenation, and it is simple to expand the channel dimension with a small additional computational cost. The ShuffleNet model is formed by the ShuffleNet units. The ShuffleNet architecture is made by stacking ShuffleNet units together and classified into three stages. The first block in every stage is implemented with a stride equal to two. Other parameters in the same stage stay identical, and the number of output channels doubled for the following stage. The proposed wide-ShuffleNet develops from ShuffleNet units and the idea of skip connections. Now, we explore the skip connections. [PERSON] et al.