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Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis cou...

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Autores principales: Baghdadi, Nadiah A., Malki, Amer, Magdy Balaha, Hossam, AbdulAzeem, Yousry, Badawy, Mahmoud, Elhosseini, Mostafa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454783/
https://www.ncbi.nlm.nih.gov/pubmed/36092017
http://dx.doi.org/10.7717/peerj-cs.1054
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author Baghdadi, Nadiah A.
Malki, Amer
Magdy Balaha, Hossam
AbdulAzeem, Yousry
Badawy, Mahmoud
Elhosseini, Mostafa
author_facet Baghdadi, Nadiah A.
Malki, Amer
Magdy Balaha, Hossam
AbdulAzeem, Yousry
Badawy, Mahmoud
Elhosseini, Mostafa
author_sort Baghdadi, Nadiah A.
collection PubMed
description Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework’s adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
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spelling pubmed-94547832022-09-09 Classification of breast cancer using a manta-ray foraging optimized transfer learning framework Baghdadi, Nadiah A. Malki, Amer Magdy Balaha, Hossam AbdulAzeem, Yousry Badawy, Mahmoud Elhosseini, Mostafa PeerJ Comput Sci Bioinformatics Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework’s adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review. PeerJ Inc. 2022-08-08 /pmc/articles/PMC9454783/ /pubmed/36092017 http://dx.doi.org/10.7717/peerj-cs.1054 Text en ©2022 N Baghdadi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Baghdadi, Nadiah A.
Malki, Amer
Magdy Balaha, Hossam
AbdulAzeem, Yousry
Badawy, Mahmoud
Elhosseini, Mostafa
Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title_full Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title_fullStr Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title_full_unstemmed Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title_short Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
title_sort classification of breast cancer using a manta-ray foraging optimized transfer learning framework
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454783/
https://www.ncbi.nlm.nih.gov/pubmed/36092017
http://dx.doi.org/10.7717/peerj-cs.1054
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