## 4 Results and Performance Discussion
### Performance Analysis
V1 outperforms V2 in all cases, and the combination of V1 and RF seems to be the best across the entire validation set. It is noticeable that 'Class 1' detection accuracy is lower than in the other two classes. We assume that some micro-cracks and finger failures are falsely predicted as 'No failures'. The reasons for this false prediction differ between the two vectors. As the intensity and contrast of Class 1 are similar to healthy cells, feature vector V1 is less sensitive to this fault. When V2 is used, it seems that as Class 1 failures affect only a relatively small percentage of the acquired image, they are sometimes classified as statistical noise. Figure 6 also displays the computed accuracy for each of the computed ML classifiers. The overall accuracy values (see Table 3) give an unbiased estimate for correctly predicting the images' actual class labels from the final tuned algorithm. It should be noted that the weighted average accuracy of all the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Article** & **Method** & **Classifier** & **F1 score (\%)** & **Accuracy (\%)** & **Detected Defects** & **EL Cell Images** (Dataset)** \\ \hline \multirow{3}{*}{This study} & Statistical & RF & 98.3 & 99.6 & Cracks B and C & \\  & parameters (V1) & k-NN & 97.1 & 98.5 & micro-crack A & 2185 \\  & & SVM & 94.3 & 96.7 & finger failures & \\  & & & & & Cracked, busbar & \\
[19] & Haralicks & SVM & 98 & 98.9 & corroded, edge and & 6264 \\  & features & CNN & 97 & 98.2 & & \\  & Spectral clustering & & & & & \\
[21] & ROI location & k-mean method & 92.1 & 99.1 & Interrupted finger defects & — \\  & Stochastic gradient descent & SVM & — & 98.7 & & \\  & & MLP-ANN & — & 98.1 & Cracked, corroded & 14,200 \\  & & RF & — & 96.9 & & \\
[20] & NAG based learning & CNN & — & 98.4 & Cracks (normal, linear, cross, & 6120 \\  & Isolated deep & & & & & \\
[8] & learning & CNN & 91.9 & 93 & Different defects & \(>\)7872 \\  & Transfer & & & & Material defects, grid fingers, & \\  & learning via & & & & deep and microcracks, & \\
[14] & t-SNE & CNN & 88.4 & 88.4 & cell degradation & 2624 \\  & & & & & Material defects, grid fingers, & \\  & & & & & deep and microcracks, & \\
[14] & Kaze/VGG & SVM & 82.5 & 82.4 & cell degradation & 2624 \\  & Hough region detection & SVM & 5.1 & 99.7 & micro-crack A & 47,244 \\  & RF & 4.4 & 96.7 & finger failures & \\  & & & & Cracks B and C & \\
[22] & Percentile region detection & RF & 6.6 & 96.5 & micro-crack A & 47,244 \\  & SVM & 4.1 & 99.7 & finger failures & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the performance of various automated fault classification for EL images. classes (92.1% (SVM), 95.4% (RF), and 94.9% (k-NN)) is lower than the overall accuracy (96.7% (SVM), 99.2% (RF), 98.4% (k-NN)) for the V2 feature vector. It gives equal contributions to the three classes' predictive performance, which are independent of their number of observations, unlike those used to compute the overall accuracy. The V1 feature vector's overall accuracy indicates that the overall performance is high compared to that of the V2 feature vector, even though the classifiers underperform in the individual class 1 of all three classes. Furthermore, other output metric parameters (precision, recall, true positives, false positives and more) are calculated (see Figure A1) and the performance of the developed vectors are evaluated. Figure 7 presents represented images with their actual and predicted labels. We analyze the falsely predicted images to evaluate the mislabels. Interestingly, many of the wrongly labeled cases are due to other failures (such as striation rings) that have not been classified in this study. The algorithms have classified these images as Class 0 or Class 1, although the actual classification (by the trainer) is Class 2 (no failure). This can be easily addressed by adding new classes. Other cases were misclassified due to a small difference between neighboring pixel values in the EL image identified as statistical noise.