Long-tail Distributed Medical Image Classification Based on Large Selective Nuclear Bilateral-branch Networks

In medical scenarios,datasets often exhibit characteristics of a long-tailed distribution,where in the imbalance may cause models to favor head classes,resulting in poorer performance in identifying tail classes and thus affecting model accuracy.Common approaches involve data augmentation to transform original data into a balanced distribution.However,the quality of augmented tail class samples is often inadequate,failing to genuinely improve the classification accuracy of tail classes.Addressing this issue,this paper proposes a large selective kernel bilateral branch network model(LSKBB).The model mainly consists of two parts:the traditional learning branch and the re-balancing branch.It adopts the LSK module to acquire key information and focus on contextual information.Additionally,a dynamic loss function is designed to enable the model to transition gradually from one focus direction to another,thereby enhancing classification accuracy.In image classification experiments conducted on medical datasets with long-tail distributions without altering their characteristics,the proposed LSKBB model shows performance improvements compared to existing methods.When the imbalance ratios are 10,50,and 100,the accuracy of the LSKBB model increases by 1.41%,1.25%,and 1.25%,respectively,on BreaKHis dataset.On ChestX-ray dataset,the accuracy increases by 6.10%,3.15%,and 2.47%,respectively.The experimental results indicate that the LSKBB model achieves good performance under different imbalance ratios and is suitable for classification and detection on medical datasets with long-tail distributions.