An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets.

Pacal, Ishak; Karaman, Ahmet; Karaboga, Dervis; Akay, Bahriye; Basturk, Alper; Nalbantoglu, Ufuk; Coskun, Seymanur

Pacal, Ishak; Karaman, Ahmet; Karaboga, Dervis; Akay, Bahriye; Basturk, Alper; Nalbantoglu, Ufuk; Coskun, Seymanur.

Afiliação

Pacal I; Computer Engineering Department, Engineering Faculty, Igdir University, Igdir, Turkey. Electronic address: ishak.pacal@igdir.edu.tr.
Karaman A; Gastroenterology Department, Acibadem Hospital, Kayseri, Turkey.
Karaboga D; Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey.
Akay B; Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey.
Basturk A; Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey.
Nalbantoglu U; Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey.
Coskun S; Gastroenterology Department, Acibadem Hospital, Kayseri, Turkey.

Comput Biol Med ; 141: 105031, 2022 02.

Article em En | MEDLINE | ID: mdl-34802713

Assuntos

Pólipos do Colo; Algoritmos; Inteligência Artificial; Pólipos do Colo/diagnóstico; Colonoscopia; Humanos; Redes Neurais de Computação

Palavras-chave

Colon cancer; Colorectal cancer; Convolutional neural networks; Deep learning; Etis-Larib dataset; Medical image analysis; Negative samples; PICCOLO polyp dataset; Polyp detection; Real-time polyp detection; Rectal cancer; SUN polyp dataset; Scaled-YOLOv4; YOLOv3; YOLOv4; YOLOv4-CSP

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pólipos do Colo Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Comput Biol Med Ano de publicação: 2022 Tipo de documento: Article

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