Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks.

Ramana, Kadiyala; Kumar, Madapuri Rudra; Sreenivasulu, K; Gadekallu, Thippa Reddy; Bhatia, Surbhi; Agarwal, Parul; Idrees, Sheikh Mohammad

Ramana, Kadiyala; Kumar, Madapuri Rudra; Sreenivasulu, K; Gadekallu, Thippa Reddy; Bhatia, Surbhi; Agarwal, Parul; Idrees, Sheikh Mohammad.

Afiliación

Ramana K; Department of Information Technology (IT), Chaitanya Bharathi Institute of Technology, Hyderabad, India.
Kumar MR; Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India.
Sreenivasulu K; Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India.
Gadekallu TR; Department of Information Technology, Vellore Institute of Technology, Vellore, India.
Bhatia S; Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hasa, Saudi Arabia.
Agarwal P; Department of Computer Science and Engineering (CSE), Jamia Hamdard, India.
Idrees SM; Department of Computer Science Institutt for datateknologi og informatikk (IDI), Norwegian University of Science and Technology, Gjøvik, Norway.

Front Oncol ; 12: 886739, 2022.

Article en En | MEDLINE | ID: mdl-35785184

Palabras clave

DenseNet; VGG-16; computer tomography (CT) scan images; inception models; pre-trained models; saliency segmentation; whale optimization

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza

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