{"title":"用于肺结节分割的深度学习模型:比较研究","authors":"Aliya Orazalina, Heechul Yoon, Sang-II Choi, Seokhyun Yoon","doi":"10.1007/s42835-024-02032-1","DOIUrl":null,"url":null,"abstract":"<p>Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":"2016 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning Models for Lung Nodule Segmentation: A Comparative Study\",\"authors\":\"Aliya Orazalina, Heechul Yoon, Sang-II Choi, Seokhyun Yoon\",\"doi\":\"10.1007/s42835-024-02032-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.</p>\",\"PeriodicalId\":15577,\"journal\":{\"name\":\"Journal of Electrical Engineering & Technology\",\"volume\":\"2016 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Engineering & Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42835-024-02032-1\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-02032-1","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Deep Learning Models for Lung Nodule Segmentation: A Comparative Study
Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.
期刊介绍:
ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies.
The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.