CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called...

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Autores:
Li, Yupeng
Zhao, Dong
Ma, Chao
Escorcia Gutierrez, José
Aljehane, Nojood O.
Ye, Xia
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/13355
Acceso en línea:
https://hdl.handle.net/11323/13355
Palabra clave:
Benchmark
COVID-19
Medical diagnosis
Image segmentation
Population-based method
RIME
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embargoedAccess
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
id RCUC2_83c353b58778f893582df1305ba9ce41
oai_identifier_str oai:repositorio.cuc.edu.co:11323/13355
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
title CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
spellingShingle CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
Benchmark
COVID-19
Medical diagnosis
Image segmentation
Population-based method
RIME
title_short CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
title_full CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
title_fullStr CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
title_full_unstemmed CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
title_sort CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images
dc.creator.fl_str_mv Li, Yupeng
Zhao, Dong
Ma, Chao
Escorcia Gutierrez, José
Aljehane, Nojood O.
Ye, Xia
dc.contributor.author.none.fl_str_mv Li, Yupeng
Zhao, Dong
Ma, Chao
Escorcia Gutierrez, José
Aljehane, Nojood O.
Ye, Xia
dc.subject.proposal.eng.fl_str_mv Benchmark
COVID-19
Medical diagnosis
Image segmentation
Population-based method
topic Benchmark
COVID-19
Medical diagnosis
Image segmentation
Population-based method
RIME
dc.subject.proposal.none.fl_str_mv RIME
description To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-12-15
dc.date.accessioned.none.fl_str_mv 2024-09-23T20:38:46Z
dc.date.available.none.fl_str_mv 2024-12-15
2024-09-23T20:38:46Z
dc.type.none.fl_str_mv Artículo de revista
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dc.type.content.none.fl_str_mv Text
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dc.identifier.citation.none.fl_str_mv Yupeng Li, Dong Zhao, Chao Ma, José Escorcia-Gutierrez, Nojood O. Aljehane, Xia Ye, CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images, Computers in Biology and Medicine, Volume 169, 2024, 107838, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2023.107838.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/13355
dc.identifier.doi.none.fl_str_mv 10.1016/j.compbiomed.2023.107838
identifier_str_mv Yupeng Li, Dong Zhao, Chao Ma, José Escorcia-Gutierrez, Nojood O. Aljehane, Xia Ye, CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images, Computers in Biology and Medicine, Volume 169, 2024, 107838, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2023.107838.
10.1016/j.compbiomed.2023.107838
url https://hdl.handle.net/11323/13355
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.none.fl_str_mv Computers in Biology and Medicine
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 Published by Elsevier Ltd.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfLi, YupengZhao, DongMa, ChaoEscorcia Gutierrez, JoséAljehane, Nojood O.Ye, Xia2024-09-23T20:38:46Z2024-12-152024-09-23T20:38:46Z2023-12-15Yupeng Li, Dong Zhao, Chao Ma, José Escorcia-Gutierrez, Nojood O. Aljehane, Xia Ye, CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images, Computers in Biology and Medicine, Volume 169, 2024, 107838, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2023.107838.https://hdl.handle.net/11323/1335510.1016/j.compbiomed.2023.107838To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.56 páginasapplication/pdfengElsevier LtdUnited KingdomCDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray imagesArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Computers in Biology and MedicineSimos, T.E. Real-time estimation of R0 for COVID-19 spread (2021) Mathematics, 9 (6), p. 664.Li, Q. Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China (2020) J. Affect. Disord., 277, pp. 153-158.Xie, X. Evaluating cancer-related biomarkers based on pathological images: a systematic review (2021) Front. Oncol., 11.Lin, Q. A novel approach of surface texture mapping for cone-beam computed tomography in image-guided surgical navigation (2023) IEEE J. Biomed. Health Inform.Motta, P.C. Automatic COVID-19 and common-acquired pneumonia diagnosis using chest CT scans (2023) Bioengineering, 10 (5), p. 529.Gao, Z. Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning (2022) Br. J. Ophthalmol., bjophthalmol-2022Li, M. Explainable COVID-19 infections identification and delineation using calibrated pseudo labels (2023) IEEE Trans. Emerg. Topics Comput. Intellig., 7 (1), pp. 26-35.Maguolo, G., Nanni, L. A critic evaluation of methods for covid-19 automatic detection from x-ray images (2021) Inf. Fusion, 76, pp. 1-7.Ye, Q. Robust weakly supervised learning for COVID-19 recognition using multi-center CT images (2022) Appl. Soft Comput., 116.Hu, S. Weakly supervised deep learning for COVID-19 infection detection and classification from CT images (2020) IEEE Access, 8, pp. 118869-118883.Su, H. Multilevel threshold image segmentation for COVID-19 chest radiography: a framework using horizontal and vertical multiverse optimization (2022) Comput. Biol. Med., p. 146.Hao, S. Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images (2023) Biomed. Signal Process ControlSu, H. RIME: a physics-based optimization (2023) Neurocomputing, 532, pp. 183-214.Wu, G., Mallipeddi, R., Suganthan, P. Problem Definitions and Evaluation Criteria for the CEC 2017 Competition and Special Session on Constrained Single Objective Real-Parameter Optimization (2016),Price, K. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization (2018) Technical Report, Nanyang Technological University SingaporeKumar, A. Problem Definitions and Evaluation Criteria for the CEC 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization (2022)García, S. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power (2010) Inf. Sci., 180 (10), pp. 2044-2064.Derrac, J. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2011) Swarm Evol. Comput., 1 (1), pp. 3-18.Zhang, L. FSIM: a feature similarity index for image quality assessment (2011) IEEE Trans. Image Process., 20 (8), pp. 2378-2386.Huynh-Thu, Q., Ghanbari, M. Scope of validity of PSNR in image/video quality assessment (2008) Electron. Lett., 44, pp. 800-801.Zhou, W. Image quality assessment: from error visibility to structural similarity (2004) IEEE Trans. Image Process., 13 (4), pp. 600-612.Zhang, Q. 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optimization driven multi threshold.pdf.jpgGenerated Thumbnailimage/jpeg14774https://repositorio.cuc.edu.co/bitstreams/c82fed49-6f78-4753-8547-7eb0661255ff/downloaddbf03003d61e42faab3e95200ed5fa6eMD5411323/13355oai:repositorio.cuc.edu.co:11323/133552024-09-24 03:00:34.29https://creativecommons.org/licenses/by-nc-nd/4.0/© 2023 Published by Elsevier Ltd.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>


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