Abstract: Aiming at the problem that traditional manual detection is easily affected by subjective factors leading to misdiagnosis or omission in brain tumour reading,an improved YOLO model is proposed for intelligent detection of brain tumour foci region by combining the characteristics of brain tumour images.Aiming at the irregular shape of brain tumour lesion regions,deformable convolution is introduced to make the network adaptive to complex lesion morphology and improve the feature extraction ability of irregular lesions.Meanwhile,by embedding a global attention mechanism that combines global multi-attention,local attention and channel attention,the network focuses on the subtle features of the lesion region while reducing the negative impact of the complex background of the image on the feature extraction of the lesion region in order to obtain a higher recognition accuracy.In addition,for the actual situation that the prediction frames in the brain tumour dataset annotation are not necessarily accurate,the improved Wise-IoU is used instead of the original CIoU loss function to adapt to the problem of inaccurate manual annotation.The results of comparison experiments on the brain tumour dataset Brain Tumor Detection show that the proposed model improves the accuracy by 5.9％compared to the original model.

Key words: YOLO, Medical image detection, Brain tumor, Attention mechanism, Deformable convolution