# Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging
## 3 Results and Discussion
### CNN Classification Experiment Results and Analysis  
In this experiment, a convolutional neural network (CNN) was used to classify and identify photoacoustic brain tumour images. The experiment was designed to compare the classification results of the original image with the segmented tumour region in order to assess the impact of the segmentation algorithm on the classification recognition. The experimental results are shown in Table 2, and the recall of the original image is lower than that of the segmented image, indicating that the method proposed in this paper is more likely to enable the CNN to detect and identify the brain tumour information. The precision, recall, and accuracy of brain tumour image classification after the detection of segmentation were significantly improved over the original brain tumour image. Among them, the accuracy rate is improved by 2.09%, recall rate by 0.73%, and precision rate by 1.33%, which shows the effectiveness of the method proposed in this paper. Figure 10 shows the results of CNN classification training, where Figure 10a is the classification of photoacoustic brain tumour images, and Figure 10b is the classification of segmented brain tumour images. In the original photoacoustic brain tumour as in Figure 10a, the confusion between the \"positive\" category and the \"negative\" category was 0.04, and the confusion between the \"negative\" category and the \"Positive\" category was 0.08, however, in the segmented classification as in Figure 10b, the confusion between \"Positive\" category and \"Negative\" category was 0.02, \"Negative\" category and \"Negative\" category was 0.02, and \"Negative\" category was 0.08, and the confusion between the \"negative\" category was 0.08, and the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision (\%)**} & **Recall (\%)** & **Binary Accuracy (\%)** \\ \hline \multirow{3}{*}{Original image} & all & 94.12 & 94.07 & \multirow{3}{*}{94.01} \\  & positive & 96.03 & 92.31 & \\  & negative & 92.21 & 95.83 & \\ \hline \multirow{3}{*}{Segmented image} & all & 97.02 & 97.04 & \multirow{3}{*}{97.02} \\  & positive & 96.08 & 98.03 & \\ \cline{1-1}  & negative & 97.96 & 96.05 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of CNN classification. Figure 9: The detection results of the YoLov8-MedSAM model. and \"positive\" categories is 0.04. By comparing the results of Figure 10a,b, it can be observed that, under the same classification model, the binary classification confusion value of the segmented brain tumour image is reduced as compared to Figure 10a. This indicates that the segmented brain tumour image highlights the information related to the tumour more and removes the background information that is not related to the tumour, which makes the features obtained by the model in the learning process more salient and discriminative. Therefore, the segmented brain tumour image shows significant improvement on the classification task compared to the original image. According to Table 3, it can be seen that Specificity (Specificity) reflects the ability of the method to correctly identify non-tumour tissues, which increased from 92.31% to 96.08%, indicating a reduction in the false alarm rate and an enhanced ability to differentiate between normal brain tissues. Sensitivity, on the other hand, measured the ability to correctly detect tumours and improved from 94.07% to 98.03%, showing the enhanced ability of the proposed method in identifying tumours. It shows that the method proposed in this paper can provide a more reliable diagnostic tool in the application of photoacoustic imaging. Table 4 lists the various current methods for brain tumour classification, and upon comparison, it is found that the method proposed in this paper has a significant improvement in classification accuracy.