# Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging
## 4 Conclusions  
In this paper, the YoLov8-MedSAM model is proposed to achieve the adaptive detection and segmentation of photoacoustic brain tumour regions. The algorithm is able to efficiently identify and categorise tumour locations without pixel-level annotation by combining deep learning and medical image processing approaches. The experimental results show that the detection, segmentation, and classification processing of brain tumour images by the model proposed in this paper can improve the recognition and classification accuracy of brain tumours. Among them, the accuracy of the CNN classification results is improved by 3.01%, the recall rate is improved by 2.97%, and the precision rate is improved by 2.90%. In summary, the model proposed in this paper provides an efficient and accurate new method for the automatic detection and segmentation of photoacoustic brain tumour regions. The model not only helps to improve the diagnosis and treatment of brain tumours but also can provide doctors with more convenient and efficient data processing tools, which has important clinical application value. The study in this paper proposes the YoLov8-MedSAM model, which, from the experimental results, is still to be improved for photoacoustic brain tumour detection segmentation, and the model still needs to be optimised. The research in this paper still has limitations. For one thing, the different types of brain tumours were not studied in detail. To overcome this limitation, we plan to delve into the knowledge of relevant brain tumour types in future work. On the other hand, the performance of the YoLov8-MedSAM model on unseen data may be limited especially in the medical field, where the cross-institutional performance may be degraded by different hospital equipment and imaging conditions; we plan to use more brain tumour data to train the model and continuously optimise the model in future work to overcome this limitation. Therefore, we plan to collect more photoacoustic image data in future work and further annotate and optimise the data to improve the training effectiveness and generalisation ability of the deep learning model. Meanwhile, we plan to conduct more clinical experiments and validations in our future work to apply our method to clinical practice and evaluate its effectiveness and application prospects in real clinical environments. This will contribute more to further development and clinical applications in the field of photoacoustic image processing. Conceptualisation, Y.C. ; methodology, Y.C. ; software, Y.C. ; validation, Y.C., R.H., Y.J., S.Y., Y.L. and J.Z. ; data curation, Y.C. ; writing--original draft preparation, Y.C. ; writing--review and editing, Y.C., R.H., S.Y., Y.L., J.Z. and Y.J. ; visualisation, R.H. and Y.J. ; supervision, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (grant numbers 12374440). Not applicable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Data** & **Accuracy (\%)** \\ \hline Deep convolutional neural network & BraTS & 96.30 \\ model & & \\ Support Vector Machine (SVM) & Photoacoustic tumour images & 90.00 \\ Algorithms & Breast cancer images & 91.18 \\ AlexNet model & Breast cancer images & 87.69 \\ K-means algorithms & Photoacoustic breast cancer images & 82.14 \\ Deep convolutional neural network & Photoacoustic brain tumour images & 94.01 \\ model & Photoacoustic brain tumour images & 97.02 \\ The methods proposed in this paper & Photoacoustic brain tumour images & 97.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of the proposed method with others. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.