Abstract
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce and difficult to obtain, which hinders the development and application of medical image analysis models. With the advancement of artificial intelligence, particularly deep learning technologies in the field of medical imaging, new concepts and tools have been introduced for the early diagnosis, treatment planning, and prognosis evaluation of brain tumors. To address the challenge of imbalanced brain tumor datasets, we propose a novel data augmentation technique based on a diffusion model, referred to as the Multi-Channel Fusion Diffusion Model(MCFDiffusion). This method tackles the issue of data imbalance by converting healthy brain MRI images into images containing tumors, thereby enabling deep learning models to achieve better performance and assisting physicians in making more accurate diagnoses and treatment plans. In our experiments, we used a publicly available brain tumor dataset and compared the performance of image classification and segmentation tasks between the original data and the data enhanced by our method. The results show that the enhanced data improved the classification accuracy by approximately 3% and the Dice coefficient for segmentation tasks by 1.5%-2.5%. Our research builds upon previous work involving Denoising Diffusion Implicit Models (DDIMs) for image generation and further enhances the applicability of this model in medical imaging by introducing a multi-channel approach and fusing defective areas with healthy images. Future work will explore the application of this model to various types of medical images and further optimize the model to improve its generalization capabilities. We release our code at https://github.com/feiyueaaa/MCFDiffusion.