Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models

Breast cancer is a leading cause of mortality among women, underscoring the critical need for accurate and early diagnosis to enhance treatment efficacy. Traditional imaging techniques are limited in their ability to differentiate between benign and malignant lesions, particularly in the early stages, for there are very few images available for the lesion area and the resolution of these images is poor. This paper introduces a novel lesion diffusion model that dynamically amplifies lesion areas, providing a multi-frame analysis to improve classification accuracy. By integrating time-aware motion modeling, the proposed method tracks temporal changes in lesions, Generating a sequence of magnified frames highlighting subtle lesion features. Tested on the BUSI breast ultrasound dataset, our model achieved a 10.269% improvement in classification accuracy over baseline methods, with an average gain of 4.645% across multiple frames. The results demonstrate the model’s ability to enhance the claim and diagnostic utility of breast cancer images after magnification This dynamic lesion amplification approach presents a significant advancement in computer-aided breast cancer diagnostics, offering new possibilities for improving early-stage detection.