Accurate segmentation of brain tumour means that surgeons accurately remove the tumour without damaging other healthy tissues. At present, due to the differences in human brains, the widely used manual brain tumour segmentation method cannot guarantee its accuracy and reliability. Therefore, it is of great social and practical significance to work out an automatic and accurate brain tumour segmentation method based on the computeraided technology. This paper proposes a novel brain tumour segmentation method based on the deep learning model of stacked de-noising auto-coder. Firstly, by model training, it obtains the parameters of the deep learning network, and then it extracts high-level abstract features of the input image data through the network and uses these features to translate the segmentation of brain tumour to the classification of image blocks. Finally, this paper applies the proposed method for the MRI images of real brain tumour patients to carry out segmentation of brain tumours, and then compares it with the manual brain tumour segmentation method. The results show that the computer-aided brain tumour segmentation method is more effective and accurate and can provide reliable basis for the removal of brain tumours by surgeons without damaging normal tissues.

Computer-aided, Brain Tumour Detection, Brain Tumour Segmentation