# An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture

arXiv: https://arxiv.org/abs/2508.11532 · HTML: https://mlsanity.com/paper/2508.11532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture](https://mlsanity.com/paper/2311.16593/) (2023)
- [ConvNeXt-backbone HoVerNet for nuclei segmentation and classification](https://mlsanity.com/paper/2202.13560/) (2022)
- [Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture](https://mlsanity.com/paper/2404.11843/) (2024)
- [Artistic Neural Style Transfer Algorithms with Activation Smoothing](https://mlsanity.com/paper/2411.08014/) (2024)
- [Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions](https://mlsanity.com/paper/2407.12647/) (2024)
- [How to Squeeze An Explanation Out of Your Model](https://mlsanity.com/paper/2412.05134/) (2024)

## Built on by (most cited first, 1 of 1)

- [Proof of Concept for Mammography Classification with Enhanced Compactness and Separability Modules](https://mlsanity.com/paper/2512.06575/) (2025)

## Abstract (arXiv)

Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while reducing computational complexity. Specifically, the method introduces a dual global pooling (Global Average Pooling and Global Max Pooling) feature fusion strategy into the ConvNeXt-Tiny backbone to simultaneously preserve global statistical features and salient response information. A lightweight channel attention module, termed Squeeze-and-Excitation Vector (SEVector), is designed to improve the adaptive allocation of channel weights while minimizing parameter overhead. Additionally, a Feature Smoothing Loss is incorporated into the loss function to enhance intra-class feature consistency and suppress intra-class variance. Under CPU-only conditions (8 threads), the method achieves a maximum classification accuracy of 89.10% on the test set within 10 training epochs, exhibiting a stable convergence trend in loss values. Experimental results demonstrate that the proposed method effectively improves medical image classification performance in resource-limited settings, providing a feasible and efficient solution for the deployment and promotion of medical imaging analysis models.