# Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
## 3 Research Methodology
### Experimental Settings and Results
#### 3.2.2 Comparison of the MobileNet and Other CNN Architectures  
In this section of the experiments, we compared the results of MobileNet with those of other CNN architectures based on the best width multiplier and resolution parameters obtained from the previous experiment with the MobileNetV2 architecture. The resolution was set to 224 \(\times\) 224 pixels, and the width multiplier was 1.0. We tested two types of imported data: (1) data without resizing the resolution, which was 224 \(\times\) 224; the standard CNN, VGG16, VGG19, ResNet50, ResNet101, and DenseNet121 were tested; and (2) the data with a scalable resolution of 299 \(\times\) 299 based on the input model specification; Xception, InceptionV3, and InceptionResNetV2 were tested. We trained the entire model on the PiCO_V2 dataset with 15 epochs. The results are shown in Table 6.  
The results shown in Table 6 indicate that MobileNetV2 significantly outperformed all CNN models. The accuracy of MobileNetV2 was 94.05%, followed by DenseNet121, InceptionV3, Xception, VGG16, ResNet50, VGG19, InceptionResNetV2, ResNet101, and Standard CNN with accuracies of 93.53%, 93.23%, 92.82%, 91.95%, 91.65%, 91.12%, 90.93%, 90.21%, and 89.70%, respectively. The results also show that MobileNetV2 outperformed the standard CNN by approximately 4.35%.  
In terms of the model size, MobileNetV2 was still the smallest model architecture. The total size of the MobileNetV2 network was only 12.52 Mb, making it approximately 6.6 and 7.7 times smaller than DenseNet121 and InceptionV3, respectively. In terms of network size, VGG16 outperformed VGG19. VGG16 and VGG19, on the other hand, required enormous network sizes. The same problem occurred for the DenseNet, Inception, Xception, and ResNet networks with larger model sizes, and the accuracy was less than that of MobileNetV2. As a result, those models were not used in our research to solve the problem.  
Table 6 illustrates the time it took to train all models at 15 epochs. The model that took the least amount of time in training was the standard CNN approach, which took 1.17 h. MobileNetV2, VGG16, VGG19, InceptionV3, ResNet50, DenseNet121, ResNet101, Xception, and InceptionResNetV2 took approximately 5.13, 8.39, 12.19, 15.38, 16.04, 21.47, 25.42, 30.08, and 30.25 h, respectively. The results are shown in Table 6. MobileNetV2 outperformed the other models and performed well in recognizing and classifying impurities in coconut oil.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No** & **Model** & \begin{tabular}{c} **Input** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Accuracy (\%)** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Training Time** \\ **(Hour)** \\ \end{tabular} & \
\begin{tabular}{c} **Test Time** \\ **(Minute)** \\ \end{tabular} &
\begin{tabular}{c} **Size Model** \\ **(Mb)** \\ \end{tabular} \\ \hline
1 & MobileNetV2 & 224 \(\times\) 224 \(\times\) 3 & 94.05 & 5.13 & 18.20 & 12.52 \\
2 & Standard CNN & 224 \(\times\) 224 \(\times\) 3 & 89.70 & 1.17 & 18.35 & 8.27 \\
3 & Xception & 299 \(\times\) 299 \(\times\) 3 & 92.82 & 30.08 & 22.44 & 842.33 \\
4 & InceptionV3 & 299 \(\times\) 299 \(\times\) 3 & 93.23 & 15.38 & 20.22 & 96.88 \\
5 & ResNet50 & 224 \(\times\) 224 \(\times\) 3 & 91.65 & 16.04 & 19.27 & 689.54 \\
6 & ResNet101 & 224 \(\times\) 224 \(\times\) 3 & 90.21 & 25.42 & 19.37 & 795.23 \\
7 & DenseNet121 & 224 \(\times\) 224 \(\times\) 3 & 93.53 & 21.47 & 20.10 & 827.27 \\
8 & VGG16 & 224 \(\times\) 224 \(\times\) 3 & 91.95 & 8.39 & 18.56 & 689.41 \\
9 & VGG19 & 224 \(\times\) 224 \(\times\) 3 & 91.12 & 12.19 & 19.55 & 722.58 \\
10 & InceptionResNetV2 & 299 \(\times\) 299 \(\times\) 3 & 90.93 & 30.25 & 20.37 & 956.36 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the runtime performances of CNN networks on our dataset at epochs = 15.