# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Neural Network Design
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Balanced Accuracy**} & \multicolumn{2}{c}{**F1\({}_{macro}\)**} & \multicolumn{2}{c}{**Init**} & \multicolumn{2}{c}{**Block 1**} & \multicolumn{2}{c}{**Block 2**} & \multicolumn{2}{c}{**Block 3**} & \multicolumn{2}{c}{**FC\({}_{MF}\)**} & \multicolumn{1}{c}{**FC**} \\ \hline \multirow{6}{*}{**Model**} & 84.42 \(\pm\) 1.62 & 79.37 \(\pm\) 2.12 & 80.28 \(\pm\) 2.11 & 32 & 64 & & 4 & 100 \\ \cline{2-9}  & 85.31 \(\pm\) 1.33 & 80.69 \(\pm\) 2.30 & 81.48 \(\pm\) 1.95 & 32 & 128 & & 4 & 100 \\ \cline{2-9}  & **85.64 \(\pm\) 1.19** & **81.07 \(\pm\) 1.78** & **81.90 \(\pm\) 1.63** & 32 & 256 & & 4 & 100 \\ \cline{2-9}  & 84.17 \(\pm\) 1.69 & 78.72 \(\pm\) 3.38 & 79.74 \(\pm\) 2.85 & 64 & 64 & & 4 & 100 \\ \cline{2-9}  & 84.42 \(\pm\) 2.50 & 78.57 \(\pm\) 5.80 & 79.59 \(\pm\) 5.04 & 64 & 128 & & 4 & 100 \\ \cline{2-9}  & 85.46 \(\pm\) 1.07 & 80.94 \(\pm\) 1.43 & 81.71 \(\pm\) 1.39 & 64 & 256 & & 4 & 100 \\ \cline{2-9}  & 84.47 \(\pm\) 1.14 & 79.62 \(\pm\) 1.81 & 80.42 \(\pm\) 1.62 & 128 & 128 & & 4 & 100 \\ \hline \multirow{6}{*}{**Model**} & 85.99 \(\pm\) 1.37 & 83.94 \(\pm\) 1.53 & 83.20 \(\pm\) 1.58 & 32 & 64 & 128 & 4 & 100 \\ \cline{2-9}  & 86.74 \(\pm\) 1.28 & 83.83 \(\pm\) 1.74 & 83.78 \(\pm\) 1.62 & 32 & 128 & 256 & 4 & 100 \\ \cline{2-9}  & **87.72 \(\pm\) 1.32** & **84.78 \(\pm\) 1.45** & **84.89 \(\pm\) 1.61** & 32 & 128 & 512 & 4 & 100 \\ \cline{2-9}  & 86.04 \(\pm\) 0.86 & 83.08 \(\pm\) 1.45 & 82.96 \(\pm\) 1.14 & 64 & 64 & 128 & 4 & 100 \\ \cline{2-9}  & 86.26 \(\pm\) 0.97 & 82.93 \(\pm\) 1.14 & 83.09 \(\pm\) 1.14 & 64 & 128 & 512 & & 4 & 100 \\ \cline{2-9}  & 86.91 \(\pm\) 1.11 & 83.81 \(\pm\) 1.45 & 83.91 \(\pm\) 1.38 & 128 & 128 & 512 & & 4 & 100 \\ \hline \multirow{6}{*}{**Model**} & 85.70 \(\pm\) 1.67 & 84.12 \(\pm\) 1.90 & 83.03 \(\pm\) 1.94 & 32 & 64 & 128 & 512 & 4 & 100 \\ \cline{2-9}  & 85.78 \(\pm\) 1.37 & 84.32 \(\pm\) 1.76 & 83.14 \(\pm\) 1.60 & 32 & 64 & 128 & 1024 & 4 & 100 \\ \cline{2-9}  & **87.22 \(\pm\) 2.04** & **85.35 \(\pm\) 2.04** & **84.65 \(\pm\) 2.30** & 64 & 128 & 256 & 512 & 4 & 100 \\ \cline{2-9}  & 86.04 \(\pm\) 0.87 & 84.75 \(\pm\) 1.15 & 83.48 \(\pm\) 1.04 & 64 & 256 & 512 & 1024 & 4 & 100 \\ \cline{2-9}  & 86.18 \(\pm\) 1.30 & 84.71 \(\pm\) 1.28 & 83.60 \(\pm\) 1.44 & 128 & 128 & 512 & 1024 & 4 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results for CNNs with varying depths and widths. “Block #” indicates the number of filters per convolutional layer in each block. “FC” denotes the number of neurons in the fully connected layer, and “FC\({}_{MF}\)” refers to neurons for processing meta-features. achieved 87.72% accuracy, 84.78% balanced accuracy, and an \(F1_{macro}\) of 84.89%, with a total of 3,256,202 trainable parameters. The second-best configuration included three building blocks and yielded similar but slightly less stable results. While its performance was not significantly different statistically, it required substantially more computational resources and had higher variance, with 4,870,862 parameters--about 1.5 times more than the optimal two-block model. These findings highlight the importance of selecting an appropriately sized network, balancing performance with model complexity. Comparing the best neural network model to the top classical method (Random Forest), the CNN achieved approximately 1.5 percentage point higher accuracy and up to 3.8 percentage points improvement in balanced accuracy. This demonstrates that the CNN architecture provides a measurable performance advantage for this classification task, especially when dealing with more complex input data reflecting real-world conditions. Additionally, these results were statistically significantly different with a \(t\)-test with \(\alpha=0.1\).