# Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
## 3 Research Methodology
### Experimental Settings and Results
#### 3.2.1 Experiments with MobileNetV2  
In the first part of our experiment, MobileNetV2 was performed to test the differences between two hyperparameters: width multipliers and fixed resolution. The MobileNetV2 performance obtained from the PiCO_V2 dataset showed that the width multiplier values affected the accuracy and size of the resulting models, as is clearly illustrated in Table 2. There is a relation between the width and the size of the model. When the width multipliers are reduced in value, the size of the model is also reduced. Because the size of the model is smaller, the accuracy performance continuously decreases from 94.05% to only 71.54%. A test was then conducted to differentiate one hyperparameter from another, in terms of resolution, to see how the findings differed from those of the prior test. In this experiment, the best results were obtained from the previous step with a resolution of 244 \(\times\) 244 pixels, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Width Multipliers** & **Accuracy (\%)** & **Size Model (Mb)** \\ \hline
1.00 & 94.05 & 12.52 \\
0.75 & 88.72 & 8.38 \\
0.50 & 71.54 & 5.60 \\
0.25 & 74.25 & 3.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of MobileNets on different width multipliers (fixed resolution = 224). which was set to a width multiplier value of 1.0 as a constant throughout the testing process. Moreover, different image resolution values were used. Initially, the test was performed with the same image resolution size of 224 \(\times\) 224 pixels. The image resolution was scaled down three times in the PiCO_V2 dataset, to 192 \(\times\) 192, 160 \(\times\) 160, and 128 \(\times\) 128, and retrained to a new training model. The results of the image resolution scaling test are shown in Table 3. Scaling the resolution had little effect on the accuracy performance. The accuracy ranged from 94.05% to 91.22%, and changes in the resolution scaling had no effect on the model size. The hyperparameters affecting the experiments on MobilenetV2 from Tables 2 and 3 were combined, and the results are presented in Table 4. The resolution of the image at 224 \(\times\) 224 pixels and a width multiplier value of 1.0 were optimized for the MobileNetV2 network layer for the PiCO_V2 dataset. The performance of the MobileNetV2 model was then investigated using the 1000 images PiCO_V1 dataset, the results of which were already known. First, the images were exported into MobileNetV2 for testing. The confusion matrix values were used to describe the model's accuracy performance. The test results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & \multicolumn{8}{c}{**Width Multipliers**} \\ \cline{2-9}  & **Class** & **Air** & **FiberT1** & **FiberT2** & **FiberT3** & **FiberT4** & **ParticleT1** & **ParticleT2** & **ParticleT3** & **ParticleT4** & **Tissue** \\ \hline Air & **0.81** & 0.02 & 0.02 & 0.00 & 0.03 & 0.00 & 0.07 & 0.01 & 0.04 & 0.00 \\ \cline{2-9}  & FiberT1 & 0.02 & **0.83** & 0.05 & 0.01 & 0.01 & 0.03 & 0.03 & 0.00 & 0.02 & 0.00 \\ \cline{2-9}  & FiberT2 & 0.00 & 0.01 & **0.92** & 0.00 & 0.00 & 0.01 & 0.04 & 0.00 & 0.01 & 0.00 \\ \cline{2-9}  & FiberT3 & 0.04 & 0.09 & 0.03 & **0.72** & 0.04 & 0.01 & 0.05 & 0.00 & 0.02 & 0.01 \\ \cline{2-9}  & FiberT4 & 0.01 & 0.12 & 0.05 & 0.04 & **0.62** & 0.04 & 0.08 & 0.00 & 0.04 & 0.00 \\ \cline{2-9}  & ParticleT1 & 0.05 & 0.03 & 0.00 & 0.00 & 0.00 & **0.73** & 0.08 & 0.03 & 0.03 & 0.05 \\ \cline{2-9}  & ParticleT2 & 0.03 & 0.03 & 0.02 & 0.00 & 0.00 & 0.00 & **0.87** & 0.04 & 0.00 & 0.00 \\ \cline{2-9}  & ParticleT3 & 0.03 & 0.02 & 0.02 & 0.00 & 0.00 & 0.00 & 0.09 & **0.78** & 0.00 & 0.06 \\ \cline{2-9}  & ParticleT4 & 0.02 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & **0.98** & 0.00 \\ \cline{2-9}  & Tissue & 0.05 & 0.04 & 0.02 & 0.01 & 0.00 & 0.01 & 0.11 & 0.02 & 0.00 & **0.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the MobileNetV2 architecture.