Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

导出 LinkOut

翻译打印收藏纠错

作者： Patnala S R Chandra,Murty ^[1] ; Chinta,Anuradha ^[2] ; P Appala,Naidu ^[3] ; Deenababu,Mandru ^[4] ; Maram,Ashok ^[5] ; Athiraja,Atheeswaran ^[6] ; Nagalingam,Rajeswaran ^[7] ; V,Saravanan ^[8]

作者单位： Department of CSE, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, 500100, Telangana, India. ^[1] Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College (Deemed to be University), Kanuru, Vijayawada, 520007, Andhra Pradesh, India. ^[2] Department of CSE, Raghu Engineering College (Autonomous), Visakhapatnam, 531162, Andhra Pradesh, India. ^[3] Department of IT, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, 500100, Telangana, India. ^[4] Department of CSE, Malla Reddy College of Engineering, Maisammaguda, Secunderabad, 500100, Telangana, India. ^[5] Department of CSE (AIML), Malla Reddy College of Engineering, Secunderabad, India. ^[6] Department of IQAC, IMS Unison University, Dehradun, India. ^[7] Department of Computer Science, Dambi Dollo University, Dambi Dollo, Ethiopia. saravanan@dadu.edu.et. ^[8]

关键词 AUC-ROC CBIS-DDSM CNN Wisconsin Breast Cancer Database

主题词人类(Humans);乳腺肿瘤(Breast Neoplasms);女(雌)性(Female);乳房X线摄影术(Mammography);数据库, 事实型(Databases, Factual);威斯康星(Wisconsin);癌症早期检测(Early Detection of Cancer)

DOI 10.1038/s41598-024-74305-8

PMID 39487199

发布时间 2024-11-04

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交