# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 3 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Proposed Method**} & \multicolumn{4}{c}{**Grayscale Images**} \\ \cline{2-9}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline ResNet18 & 86.02 & 0.86 & 0.8602 & 0.86 & 85.43 & 0.8561 & 0.8563 & 0.8560 \\ ResNet50 & 86.32 & 0.8631 & 0.8632 & 0.8629 & 85.45 & 0.8541 & 0.8545 & 0.8540 \\ MobileNetv2 & 85.57 & 0.8567 & 0.8557 & 0.8559 & 83.02 & 0.8331 & 0.8302 & 0.8308 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance on CIFAR-10 test dataset. Figure 4: Precision, Recall and F1-score for MobileNetV2 on CIFAR-10 Dataset. Figure 3: Precision, Recall and F1-score for ResNet50 on CIFAR-10 Dataset. Figure 2: Precision, Recall, and F1-score for ResNet18 on CIFAR-10 Dataset. Figure 5: Confusion matrix results for classification: (**a**) Grayscale images given to ResNet18 as inputs, (**b**) Proposed Method outputs given to ResNet18 as inputs, (**c**) Grayscale images given to ResNet50 as inputs, (**d**) Proposed Method outputs given to ResNet50 as inputs, (**e**) Grayscale images given to MobileNet2 as inputs, and (**f**) Proposed Method outputs given to MobileNetv2 as inputs. The qualitative performance of the proposed decomposition process is illustrated in Figure 6, which shows how the original image is separated into three semantically meaningful layers: a smooth layer capturing coarse structural information, a detail layer emphasizing fine textures and edges, and a residual layer isolating noise and unstructured content. As expected, the residual layer appears mostly sparse, confirming that most relevant information is effectively captured by the first two layers. The results presented in Table 5 illustrate the impact of different input combinations on segmentation performance using the U-Net architecture. Among all configurations, the U-Net model that utilized the original image together with the detail layer achieved the highest performance across all metrics, with a Dice score of 0.9778, IoU of 0.9600, and pixel accuracy of 0.9897. This indicates that the detail component significantly enhances the model's ability to capture fine-grained structures critical for accurate medical image segmentation. \[\text{Dice}=\frac{2\times|A\cap B|}{|A|+|B|}=\frac{2\text{TP}}{2\text{TP}+ \text{FP}+\text{FN}} \tag{13}\]  
\[\text{IoU}=\frac{|A\cap B|}{|A\cup B|}=\frac{\text{TP}}{\text{TP}+\text{FP}+ \text{FN}} \tag{14}\]  
In comparison, the baseline model using only the original image achieved moderate performance (Dice: 0.8276, IoU: 0.7646, Pixel Accuracy: 0.9412), while adding only the smooth layer slightly degraded performance. Interestingly, when both the smooth and detail layers were combined with the original image, performance remained high (Dice: 0.9636), though still slightly lower than using the detail layer alone. These findings suggest that while smooth features may dilute fine details when used in isolation, they can still complement high-frequency detail features in multi-scale representations. Ultimately, the detail layer is the most influential factor in improving segmentation quality, and its integration into the input representation leads to significant gains in accuracy and robustness. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Dice Score** & **IoU** &
\begin{tabular}{c} **Pixel** \\ **Accuracy** \\ \end{tabular} \\ \hline U-Net (Original Image) & 0.8276 & 0.7646 & 0.9412 \\ U-Net (Original + Smooth Layer) & 0.7931 & 0.7417 & 0.9360 \\ U-Net (Original + Detail Layer) & 0.9778 & 0.9600 & 0.9897 \\ U-Net (Original + Smooth + Detail) & 0.9636 & 0.9355 & 0.9834 \\ \hline \hline \end{tabular}
\end{table}