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Dense Convolutional Neural Network for Detection of Cancer from CT Images

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computer...

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Autores principales: Sreenivasu, S. V. N., Gomathi, S., Kumar, M. Jogendra, Prathap, Lavanya, Madduri, Abhishek, Almutairi, Khalid M. A., Alonazi, Wadi B., Kali, D., Jayadhas, S. Arockia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236787/
https://www.ncbi.nlm.nih.gov/pubmed/35769667
http://dx.doi.org/10.1155/2022/1293548
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author Sreenivasu, S. V. N.
Gomathi, S.
Kumar, M. Jogendra
Prathap, Lavanya
Madduri, Abhishek
Almutairi, Khalid M. A.
Alonazi, Wadi B.
Kali, D.
Jayadhas, S. Arockia
author_facet Sreenivasu, S. V. N.
Gomathi, S.
Kumar, M. Jogendra
Prathap, Lavanya
Madduri, Abhishek
Almutairi, Khalid M. A.
Alonazi, Wadi B.
Kali, D.
Jayadhas, S. Arockia
author_sort Sreenivasu, S. V. N.
collection PubMed
description In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.
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spelling pubmed-92367872022-06-28 Dense Convolutional Neural Network for Detection of Cancer from CT Images Sreenivasu, S. V. N. Gomathi, S. Kumar, M. Jogendra Prathap, Lavanya Madduri, Abhishek Almutairi, Khalid M. A. Alonazi, Wadi B. Kali, D. Jayadhas, S. Arockia Biomed Res Int Research Article In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. Hindawi 2022-06-20 /pmc/articles/PMC9236787/ /pubmed/35769667 http://dx.doi.org/10.1155/2022/1293548 Text en Copyright © 2022 S. V. N. Sreenivasu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sreenivasu, S. V. N.
Gomathi, S.
Kumar, M. Jogendra
Prathap, Lavanya
Madduri, Abhishek
Almutairi, Khalid M. A.
Alonazi, Wadi B.
Kali, D.
Jayadhas, S. Arockia
Dense Convolutional Neural Network for Detection of Cancer from CT Images
title Dense Convolutional Neural Network for Detection of Cancer from CT Images
title_full Dense Convolutional Neural Network for Detection of Cancer from CT Images
title_fullStr Dense Convolutional Neural Network for Detection of Cancer from CT Images
title_full_unstemmed Dense Convolutional Neural Network for Detection of Cancer from CT Images
title_short Dense Convolutional Neural Network for Detection of Cancer from CT Images
title_sort dense convolutional neural network for detection of cancer from ct images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236787/
https://www.ncbi.nlm.nih.gov/pubmed/35769667
http://dx.doi.org/10.1155/2022/1293548
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