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An efficient attention Densenet with LSTM for lung disease detection and classification using X-ray images supported by adaptive R2-Unet-based image segmentation.

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第一作者： Sashi Kanth,Betha

第一单位： Department of ECE & CSE, Vignan's Institute of Engineering for Women, Kapujaggrajupeta, Visakhapatnam, India.

作者： Sashi Kanth,Betha ^[1] ; Dondapati Rajendra,Dev ^[2] ; Kalyani,Sunkara ^[3] ; Pradeep Vinaik,Kodavanti ^[4] ; Anusha,Putta ^[5]

作者单位： Department of ECE & CSE, Vignan's Institute of Engineering for Women, Kapujaggrajupeta, Visakhapatnam, India. ^[1] Department of CSE, Annamalai University, Chidambaram, Tamil Nadu, India. ^[2] School of Computer Science and Engineering, Vellore Institute of Technology, VIT-AP University, Amaravathi, India. ^[3] EECE Department, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India. ^[4] Department of IT, Vignan's Institute of Engineering for Women, Kapujaggrajupeta, Visakhapatnam, India. ^[5]

关键词 Lung disease detection and classification X-ray images adaptive recurrent residual U-net attention-based Densenet with long short term memory enhanced Pufferfish optimisation algorithm

医学主题词人类(Humans);肺疾病(Lung Diseases);算法(Algorithms);肺(Lung);图像处理, 计算机辅助(Image Processing, Computer-Assisted)

DOI 10.1080/13813455.2025.2524182

PMID 40590298

发布时间 2025-11-27

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