LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection

TL;DR

LSM-YOLO is a novel lightweight model designed for real-time, accurate medical ROI detection, improving feature extraction and fusion to enhance diagnostic support across various medical imaging datasets.

Contribution

The paper introduces LSM-YOLO, a new lightweight model with LAE and MSFM modules that enhances feature extraction and fusion for medical ROI detection, achieving state-of-the-art results.

Findings

Achieves 48.6% AP on pancreatic tumor dataset

Achieves 65.1% AP on blood cell detection dataset

Achieves 73.0% AP on brain tumor dataset

Abstract

In existing medical Region of Interest (ROI) detection, there lacks an algorithm that can simultaneously satisfy both real-time performance and accuracy, not meeting the growing demand for automatic detection in medicine. Although the basic YOLO framework ensures real-time detection due to its fast speed, it still faces challenges in maintaining precision concurrently. To alleviate the above problems, we propose a novel model named Lightweight Shunt Matching-YOLO (LSM-YOLO), with Lightweight Adaptive Extraction (LAE) and Multipath Shunt Feature Matching (MSFM). Firstly, by using LAE to refine feature extraction, the model can obtain more contextual information and high-resolution details from multiscale feature maps, thereby extracting detailed features of ROI in medical images while reducing the influence of noise. Secondly, MSFM is utilized to further refine the fusion of high-level semantic features and low-level visual features, enabling better fusion between ROI features and neighboring features, thereby improving the detection rate for better diagnostic assistance. Experimental results demonstrate that LSM-YOLO achieves 48.6% AP on a private dataset of pancreatic tumors, 65.1% AP on the BCCD blood cell detection public dataset, and 73.0% AP on the Br35h brain tumor detection public dataset. Our model achieves state-of-the-art performance with minimal parameter cost on the above three datasets. The source codes are at: https://github.com/VincentYuuuuuu/LSM-YOLO.