Hires-diagnoser: a dual stream medical image diagnosis framework based on multi-level resolution adaptive sensing

Improving medical image diagnosis performance relies on effectively representing features across various scales and accurately capturing local lesion characteristics and spatial context. While traditional convolutional neural networks are limited by fixed local receptive fields, hindering their ability to model global semantic relationships, transformers with self-attention mechanisms excel at capturing long-range contextual information but struggle with identifying small lesions. To overcome these challenges, this study introduces Hires-Diagnoser, a dual-stream framework for medical image diagnosis that supports multiple resolution levels. This framework combines ConvNeXt and Swin-Transformer branches in a parallel architecture. The ConvNeXt branch focuses on extracting local texture features through convolutions, while the Swin-Transformer branch captures global contextual dependencies using window-based self-attention. Additionally, a cross-modal correlation module (LCA) facilitates dynamic interaction and adaptive fusion of features across different resolutions. Experimental assessments on four datasets (RaabinWBC, Brain Tumor MRI, LC25000, and OCT-C8) demonstrated accuracy rates of 98.59%, 95.45%, 99.43%, and 95.23%, respectively, surpassing existing methods. By incorporating a cross-modal feature interaction mechanism, this framework achieves high performance and precise pathological interpretations, offering an effective solution for medical image diagnosis with certain practical implications.The source code of this proposal can be found at https://github.com/si-yuan20/hire-diagnoser.