# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 3 Research Methodology
### Comparison and Selection of Deep Learning Algorithms  
Deep learning is a subfield of machine learning that relies on the multilayered structure of mathematically modeled artificial neural networks. It can achieve exceptional performance by learning from vast amounts of data. Deep learning technology is used in the medical field for diagnoses using images [26]. Various deep learning algorithms for image processing have been employed in different domains. Representative convolutional neural networks (CNNs), ResNet-50, and VGG-16 have been compared and analyzed. [PERSON] et al. developed an automated identification and classification system using a CNN algorithm for switch defect detection [27]. CNNs have demonstrated exceptional performance in image and pattern recognition tasks. [PERSON] et al. introduced the innovative deep learning architecture called VGG-Net in 2014 [28]. This architecture has shown remarkable performance in image recognition and classification tasks. VGG-16 is the most popular version of VGG-Net and consists of 16 layers, 13 of which are convolutional. The architecture of VGG-16 consists of convolutional, pooling, and fully connected layers. VGG-16 extensively used small-sized filters in its pooling layers to extract diverse features from data. In addition, it has a regular structure comprising convolutional and pooling layers. The convolution layers use 3 \(\times\) 3 filters with padding, while the pooling layers use 2 \(\times\) 2 filters. The fully connected layers consist of three layers. After convolution and pooling, the feature maps are flattened into one-dimensional vectors for image classification. Unlike convolutional layers, where neurons are only connected to a local input region, fully connected layers connect every neuron to all the neurons in the previous layer. This allows each neuron to learn its own set of weights independent of the inputs. The learned weights are then used to combine the features of the image and make a final prediction. The output of the last fully connected layer is passed through the soft-max function to convert it into probability values that determine the class to which the input image belongs. This structure is easy to implement and extend, enabling consistent processing for various image sizes. VGG-16 was pretrained on the ImageNet dataset and achieved high performance in image classification tasks. The VGG-16 algorithm was selected for our study because it outperformed ResNet-50. Table 2 compares and analyzes the performance of three commonly used algorithms in image analysis: the CNN (Convolutional Neural Network), VGG-16, and ResNet-50. The performance metrics of the CNN algorithm exhibited an accuracy of 0.875, loss value of 0.384, specificity of 0.586, and recall of 0.671. When comparing VGG-16 and ResNet-50, most model performance metrics were relatively similar. However, the performance of the VGG-16 algorithm was slightly better than that of ResNet-50. Finally, the comparative results indicated that the VGG-16 algorithm outperformed the CNN and ResNet-50 regarding relative performance. VGG-16, pretrained on the ImageNet dataset, demonstrated excellent performance in image classification tasks; therefore, the VGG-16 algorithm was chosen for application in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Loss** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC-ROC** \\ \hline VGG-16 & 0.053 & 0.982 & 0.981 & 0.985 & 0.983 \\ ResNet-50 & 0.052 & 0.981 & 0.979 & 0.984 & 0.982 \\ Basic CNN & 0.384 & 0.875 & 0.586 & 0.671 & 0.511 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of algorithms. A model for discriminating cell defects was developed based on the VGG-16. The accuracy and loss values were analyzed to validate the generated model. The fitness of the model was determined based on the performance metrics. The results of the performance metrics, accuracy, and loss are shown in the graphs below.