波谱学杂志2026，Vol.43Issue(1)：71-86,16.DOI:10.11938/cjmr20253158
轻量化AD-Net模型用于颅内肿瘤MRI图像的分类研究
A Lightweight AD-Net Model for the Classification of Intracranial Tumors in MRI Images
摘要
Abstract
Intracranial tumors represent a serious neurological disorder,and early detection is critical for improving patient survival rates.However,current deep learning models for intracranial tumor image classification often suffer from insufficient feature extraction,high model complexity,and class imbalance.To address these challenges,this study proposes a lightweight deep learning architecture,the adaptive dynamic network(AD-Net).The network innovatively incorporates a dynamic convolution mechanism that adaptively adjusts filter responses,thereby enhancing the representation of complex and imbalanced tumor features.Additionally,the integration of a channel attention mechanism enables the model to focus on critical channel information,further improving classification accuracy and interpretability.This study also introduces a combined binary and ternary classification training strategy,which significantly reduces training time and computational resource requirements,making the model more suitable for resource-constrained medical settings.Experimental results demonstrate that AD-Net outperforms existing mainstream deep learning models in accuracy,precision,recall,F1 score,and Cohen's Kappa coefficient,confirming its effectiveness and practical value for intracranial tumor classification.关键词
颅内肿瘤分类/卷积神经网络/动态卷积/通道注意力机制/轻量化模型Key words
intracranial tumor classification/convolutional neural network(CNN)/dynamic convolution/channel attention mechanism/lightweight model分类
数理科学引用本文复制引用
向朝,随力,张昊天,段梦雨,刘卓睿..轻量化AD-Net模型用于颅内肿瘤MRI图像的分类研究[J].波谱学杂志,2026,43(1):71-86,16.基金项目
国家自然科学基金资助项目(11179015,51173108) （11179015,51173108）
上海理工大学科技发展项目(2019KJFZ239,2020KJFZ232). （2019KJFZ239,2020KJFZ232）
