## 5 Results and Discussion
### Efficiency Analysis  
* FLOPs and Trainable Parameters  
To demonstrate the effectiveness of the ASDC method in improving the efficiency of biomarker extraction, the computational complexity of the proposed model was evaluated using FLOPs (floating point operations per second) and number of trainable parameters.  
Figure 11: RV segmentation results on OCTA-500 scans: (**a**) input image, (**b**) ground truth, (**c**) ASDC segmented image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **Target** & **Methods** & **Dice (\%)** & **Jac (\%)** \\ \hline \multirow{8}{*}{OCTA\_6M} & & U-Net & 86.52 & 77.32 \\  & & R2U-Net4 & 81.22 & 69.29 \\  & & AttU Net5 & 86.47 & 77.23 \\ \cline{1-1}  & RV segmentation & CE-Net6 & 83.04 & 72.04 \\ \cline{1-1}  & & Cs2-Net4 & 86.13 & 76.71 \\ \cline{1-1}  & & FARGO & 89.15 & 80.50 \\ \cline{1-1}  & & **Ours** & **91.60** & **85.20** \\ \cline{1-1}  & RV and FAZ & **Ours** & **86.64** & **79.04** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of RV segmentation results on the OCTA 500 dataset.  
The number of FLOPs required by a neural network model is an important metric for evaluating its computational efficiency. FLOPs correspond to the number of arithmetic operations (such as additions and multiplications) performed by the model per input sample. We obtained the number of FLOPs required for a single forward pass on a \(400\times 400\) input image is approximately 17.70 billion or (17.70 G). This makes it relatively lightweight and computationally efficient, which can be beneficial for limited computational resources.  
The number of trainable parameters in our model is approximately 0.11 million (or 0.11 M) which is a relatively small number compared to other deep learning models. This small number of parameters means that the model requires less memory to store its parameters and is less prone to overfitting to the training data. The U-Net model has a high number of trainable parameters (up to 31 million) and requires a significant number of FLOPs to process input images, resulting in a large model size and high computational complexity. This can make real-time implementation of U-Net models challenging, especially on resource-constrained hardware [43]. Overall, the combination of low FLOPs and a smaller number of trainable parameters makes the ASDC method an efficient and compact alternative to other more complex neural network models, while still being able to extract useful biomarkers from the input images. This can be particularly advantageous in medical applications where computational efficiency and model size are critical factors for real-time analysis and diagnosis.  
* Consumed Time  
To evaluate the performance of our ASDC using the time consumed for a single image to be segmented, we performed execution time analysis as shown in Figure 13, which is the time consumed for a program or algorithm to complete its task, and it is essential for evaluating the performance of an automatic retinal image processing algorithm. We used the Google Colab environment, and our study found that our ASDC approach is faster than traditional K-means clustering for processing OCTA images of 400 \(\times\) 400 resolution. This is because deep learning models can automatically learn the relevant features and patterns from large datasets, making them more efficient for handling complex and high-dimensional data. Therefore, our ASDC approach may be a more suitable and efficient method for ophthalmologists to process OCTA images and detect relevant biomarkers with less computational time. The ASDC method took less than a second to segment the OCTA images, with the shortest time being 0.415 s.  
Figure 12: RV segmentation performance metrics on OCTA-500 scans.