A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans

Irash Perera; Uthayasanker Thayasivam

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arXiv · 2510.09107

A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans

Irash Perera·Uthayasanker Thayasivam

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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.

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Irash Perera, Uthayasanker Thayasivam. 2025-10-27. A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans. https://arxiv.org/abs/2510.09107

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