{"title":"MGScoliosis: Multi-grained scoliosis detection with joint ordinal regression from natural image","authors":"","doi":"10.1016/j.aej.2024.10.066","DOIUrl":null,"url":null,"abstract":"<div><div>Scoliosis is among the most prevalent diseases affecting teenagers. However, traditional scoliosis screening methods often resort to physical examination or radiographic imaging. The two ways both rely on experts with high costs, which are not suitable for wide-range screening. Besides, estimating Cobb angle level only using natural images are challenging. To tackle these issues, we propose a multi-grained scoliosis detection framework by jointly estimating severity level and Cobb angle level of scoliosis from a natural image instead of a radiographic image, which has not been explored before. Specifically, we regard scoliosis estimation as an ordinal regression problem, and transform it into a series of binary classification sub-problems. Besides, we adopt the visual attention network with large kernel attention as the backbone for feature learning, which can model local and global correlations with efficient computations. The feature learning and the ordinal regression is put into an end-to-end framework, in which the two tasks of scoliosis severity level estimation and scoliosis angle level estimation are jointly learned and can contribute to each other. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economical solution to wide-range scoliosis screening. Particularly, our approach achieves accuracies of 94.90% and 79.62% in estimating severity level and Cobb angle level, improving large margins of 4.90% and 12.15% over existing natural image based scoliosis detection performance, respectively. The code is available at <span><span>https://github.com/RuiChen-stack/MGScoliosis</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016824012225","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Scoliosis is among the most prevalent diseases affecting teenagers. However, traditional scoliosis screening methods often resort to physical examination or radiographic imaging. The two ways both rely on experts with high costs, which are not suitable for wide-range screening. Besides, estimating Cobb angle level only using natural images are challenging. To tackle these issues, we propose a multi-grained scoliosis detection framework by jointly estimating severity level and Cobb angle level of scoliosis from a natural image instead of a radiographic image, which has not been explored before. Specifically, we regard scoliosis estimation as an ordinal regression problem, and transform it into a series of binary classification sub-problems. Besides, we adopt the visual attention network with large kernel attention as the backbone for feature learning, which can model local and global correlations with efficient computations. The feature learning and the ordinal regression is put into an end-to-end framework, in which the two tasks of scoliosis severity level estimation and scoliosis angle level estimation are jointly learned and can contribute to each other. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economical solution to wide-range scoliosis screening. Particularly, our approach achieves accuracies of 94.90% and 79.62% in estimating severity level and Cobb angle level, improving large margins of 4.90% and 12.15% over existing natural image based scoliosis detection performance, respectively. The code is available at https://github.com/RuiChen-stack/MGScoliosis.
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering