# Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging
###### Abstract  
A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours. photoacoustic imaging; deep learning; image detection segmentation; image classification; brain tumours +
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to the image. [PERSON] et al. [15] proposed a new method for MR brain image segmentation based on 2D corpus callosum segmentation, which is semiautonomous and requires a user interruption to initialise the seed contour for correct segmentation. The proposed method has less computational time and relies on sufficient intensity selection using a Canny edge detection operator. [PERSON] et al. [16] proposed an unsupervised MRI segmentation method based on Self-Organising Feature Mapping (SOFM), which is combined with a Markov Random Field (MRF) model to extract the redundant spatial pixel region information. By combining MRF and SOFM, it is possible to accurately detect MR abnormal regions in the image. Deep learning methods have been widely used in brain tumour detection and classification. [PERSON] et al. [17] used pre-trained Inception-v3 and DenseNet201 deep learning models to extract features by concatenation with the help of the softmax classifier for the brain tumour detection and classification task. [PERSON] et al. [18] acquired MRI T1-weighted images and used a semiautomated segmentation method to analyse the likelihood of brain tumours using an active contour model, and the data showed that the edge-free morphologically active contour (MGAC) outperformed the other methods in terms of performance. [PERSON]. [PERSON] and [PERSON] [19], on the other hand, applied Gabor transform as well as soft clustering and hard clustering techniques to detect the edges of CT and MRI images, and K-mean clustering was used to group the similar features and represent the histogram attributes of the images with fuzzy c-means. [PERSON] et al. [14] proposed an SVM algorithm based on SIFT feature extraction and K-mean clustering for classifying and recognising photoacoustic breast cancer images while applying deep learning algorithms. AlexNet and GoogLeNet achieved recognition accuracies of about 87.69% and 91.18%, respectively. Traditional algorithms need to rely on predefined rules and features and lack adaptability to images [20]. In deep learning algorithms, there are problems of low recognition accuracy, high training complexity, and inapplicability to polymorphic target detection and recognition. To address this problem, this paper proposes the YoLov8-MedSAM model for the adaptive detection segmentation of brain tumour images, which can effectively distinguish brain tumours from normal brain tissue and provide more comprehensive morphological features of tumours. The segmented brain tumours were classified and identified using a CNN [21; 22; 23; 24].