# LGLD-Net: A Breakthrough in Lightweight Global-Local Dual-Branch Neural Network for Medical Microscopic Image Classification

> A lightweight neural network with only 161K parameters. By fusing local texture features and global context information, it achieves accuracy rates of 99.69% and 96.50% in acute lymphoblastic leukemia and blood cell classification tasks respectively, and can perform real-time inference at 17.56 FPS on Raspberry Pi 5.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-08-11T12:17:54.000Z
- 最近活动: 2026-08-11T12:23:23.653Z
- 热度: 150.9
- 关键词: 医学图像分类, 轻量级CNN, 显微图像分析, 边缘计算, 计算机辅助诊断, 双分支神经网络, Involution, 傅里叶特征提取
- 页面链接: https://www.zingnex.cn/en/forum/thread/lgld-net
- Canonical: https://www.zingnex.cn/forum/thread/lgld-net
- Markdown 来源: floors_fallback

---

## Introduction: LGLD-Net's Breakthrough in Lightweight Dual-Branch Neural Network for Medical Microscopic Image Classification

LGLD-Net is a lightweight global-local dual-branch neural network with only 161K parameters. By fusing local texture features and global context information, it achieves an accuracy of 99.69% in acute lymphoblastic leukemia classification tasks and 96.50% in blood cell classification tasks. It can also perform real-time inference at 17.56 FPS on Raspberry Pi 5, providing a new solution for edge deployment of medical microscopic image analysis.

## Background and Challenges: Core Difficulties in Medical Microscopic Image Analysis

Microscopic image analysis is crucial in medical diagnosis, but traditional deep learning models face two major challenges: First, it is difficult to capture both fine-grained local texture features (such as cell morphology details) and global context information (such as spatial relationships between cells) simultaneously; Second, high-performance models have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices, which limits the realization of on-site rapid diagnosis.

## Core Architecture Design: Innovative Dual-Branch Fusion Scheme

LGLD-Net addresses the above problems through a dual-branch architecture:
1. **Local Feature Aggregator (LFA)**：Uses an Involution-based Patch Stem (IPS). Involution shares parameters across channels and is independent in space, making it suitable for capturing cell micro-textures (e.g., nucleus shape, cytoplasm distribution);
2. **Global Context Extractor (GCE)**：Uses a Fourier Transform Feature Extraction Module (FFM), which converts images to the frequency domain to capture long-range dependencies and global tissue patterns;
3. **Adaptive Fusion Module (LGFM)**：Dynamically balances the contributions of local and global features, automatically adjusting weights based on the characteristics of input images.
The total number of model parameters is 161K, with a computational cost of only 1.88 GFLOPs.

## Experimental Validation and Performance: Dataset and Edge Deployment Results

### Dataset Performance
- **Acute Lymphoblastic Leukemia (ALL) Dataset**：Classification accuracy reaches 99.69%;
- **Raabin-WBC White Blood Cell Dataset**：Multi-class classification accuracy reaches 96.50%.
### Edge Device Deployment
Achieves real-time inference speed of 17.56 FPS on Raspberry Pi 5, supporting local rapid acquisition of AI-assisted diagnosis results without relying on the cloud.

## Technical Highlights and Innovative Significance: Clinical Value of Lightweight and Efficiency

1. **Balance between Parameter Count and Efficiency**：161K parameters are far lower than ResNet-50 (25 million) and EfficientNet-B0 (5.3 million), achieving comparable or better performance with less than 1% of the parameters;
2. **Adaptation to Medical Images**：Involution captures cell details, while Fourier transform models global tissue patterns, fitting the characteristics of microscopic images;
3. **Feasibility of Clinical Deployment**：The real-time inference capability of edge devices can support scenarios such as remote area clinics and mobile medical vehicles, improving medical equity.

## Limitations and Future Directions: Areas for Improvement to Explore

1. **Generalization Ability Verification**：Currently only validated on blood-related microscopic images; needs to be extended to other types such as histopathological sections and cytological smears;
2. **Interpretability Enhancement**：The decision-making process of the adaptive fusion module needs to be strengthened to enhance doctors' trust;
3. **Task Expansion**：Can explore expansion to object detection (abnormal cell localization) or segmentation tasks (cell boundary outlining).

## Summary and Insights: Lightweight Trend in Medical AI

LGLD-Net represents the "small but refined" design trend in the medical AI field, proving that well-designed architecture can surpass parameter stacking strategies. For researchers, it demonstrates the possibility of fusing traditional signal processing (Fourier transform) with deep learning (Involution); For medical practitioners, it means that lightweight and fast AI-assisted tools will gradually become popular. In the future, they are expected to be deployed on smartphones and portable devices to achieve anytime, anywhere intelligent medical care.