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Deep transfer learning approaches for bleeding detection in endoscopy images.
Caroppo, Andrea; Leone, Alessandro; Siciliano, Pietro.
Afiliación
  • Caroppo A; Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce 73100, Italy. Electronic address: andrea.caroppo@cnr.it.
  • Leone A; Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce 73100, Italy. Electronic address: alessandro.leone@cnr.it.
  • Siciliano P; Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce 73100, Italy. Electronic address: pietro.siciliano@le.imm.cnr.it.
Comput Med Imaging Graph ; 88: 101852, 2021 03.
Article en En | MEDLINE | ID: mdl-33493998
Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed rapidly over the last several years. One of the main limiting factors using this technology is that it produces a huge number of images, whose analysis, to be done by a doctor, is an extremely time-consuming process. In this research area, the management of this problem has been addressed with the development of Computer-aided Diagnosis systems thanks to which the automatic inspection and analysis of images acquired by the capsule has clearly improved. Recently, a big advance in classification of endoscopic images is achieved with the emergence of deep learning methods. The proposed expert system employs three pre-trained deep convolutional neural networks for feature extraction. In order to construct efficient feature sets, the features from VGG19, InceptionV3 and ResNet50 models are then selected and fused using the minimum Redundancy Maximum Relevance method and different fusion rules. Finally, supervised machine learning algorithms are employed to classify the images using the extracted features into two categories: bleeding and nonbleeding images. For performance evaluation a series of experiments are performed on two standard benchmark datasets. It has been observed that the proposed architecture outclass the single deep learning architectures, with an average accuracy in detection bleeding regions of 97.65 % and 95.70 % on well-known state-of-the-art datasets considering three different fusion rules, with the best combination in terms of accuracy and training time obtained using mean value pooling as fusion rule and Support Vector Machine as classifier.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Endoscopía Capsular Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Comput Med Imaging Graph Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Endoscopía Capsular Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Comput Med Imaging Graph Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos