Abstract
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis, especially for identifying subtle pathological features. This paper introduces a novel multi-branch ConvNeXt architecture designed specifically for the nuanced challenges of medical image analysis. While applied here to the specific problem of COVID-19 diagnosis, the methodology offers a generalizable framework for classifying a wide range of pathologies from CT scans. The proposed model incorporates a rigorous end-to-end pipeline, from meticulous data preprocessing and augmentation to a disciplined two-phase training strategy that leverages transfer learning effectively. The architecture uniquely integrates features extracted from three parallel branches: Global Average Pooling, Global Max Pooling, and a new Attention-weighted Pooling mechanism. The model was trained and validated on a combined dataset of 2,609 CT slices derived from two distinct datasets. Experimental results demonstrate a superior performance on the validation set, achieving a final ROC-AUC of 0.9937, a validation accuracy of 0.9757, and an F1-score of 0.9825 for COVID-19 cases, outperforming all previously reported models on this dataset. These findings indicate that a modern, multi-branch architecture, coupled with careful data handling, can achieve performance comparable to or exceeding contemporary state-of-the-art models, thereby proving the efficacy of advanced deep learning techniques for robust medical diagnostics.
Abstract (translated)
智能分析医学影像在辅助临床诊断中扮演着关键角色，特别是在识别细微的病理特征方面。本文介绍了一种新颖的多分支ConvNeXt架构，该架构专门针对医疗图像分析中的复杂挑战而设计。虽然这项研究主要应用于新冠肺炎（COVID-19）诊断这一具体问题上，但所提出的方法提供了一个通用框架，可用于从CT扫描中分类各种病理类型。提出的模型包括一个严格端到端的流程，涵盖了细致的数据预处理和增强以及一种严谨的两阶段训练策略，该策略有效利用了迁移学习的优势。该架构独特地整合了三种平行分支提取的功能：全局平均池化、全局最大池化及一个新的注意力加权池化机制。模型是在由两个不同数据集合并而成的包含2,609个CT切片的数据集上进行训练和验证的。实验结果表明，在验证集中取得了卓越的表现，最终达到了ROC-AUC为0.9937、验证准确率为0.9757及COVID-19病例F1分数为0.9825的结果，超过了在此数据集上的所有先前报告模型的表现。这些发现表明，现代多分支架构结合仔细的数据处理可以达到与当代最先进的模型相媲美或超过的性能水平，从而证明了高级深度学习技术在医学诊断中的有效性。
URL
https://arxiv.org/abs/2510.09107
