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

• URL: http://arxiv.org/abs/2510.09107v2
• Date: Mon, 27 Oct 2025 07:25:00 GMT
• Title: A Novel Multi-branch ConvNeXt Architecture for Identifying Subtle Pathological Features in CT Scans
• Authors: Irash Perera, Uthayasanker Thayasivam,
• Abstract summary: This paper introduces a novel multi-branch ConvNeXt architecture designed specifically for the nuanced challenges of medical image analysis.<n>The proposed model incorporates a rigorous end-to-end pipeline, from meticulous data preprocessing to augmentation to a disciplined two-phase training strategy.<n> 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.
• Score: 1.2461503242570642
• License: http://creativecommons.org/licenses/by/4.0/
• 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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