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BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features d...

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Autores principales: Rehman, Shams ur, Khan, Muhamamd Attique, Masood, Anum, Almujally, Nouf Abdullah, Baili, Jamel, Alhaisoni, Majed, Tariq, Usman, Zhang, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178634/
https://www.ncbi.nlm.nih.gov/pubmed/37175009
http://dx.doi.org/10.3390/diagnostics13091618
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author Rehman, Shams ur
Khan, Muhamamd Attique
Masood, Anum
Almujally, Nouf Abdullah
Baili, Jamel
Alhaisoni, Majed
Tariq, Usman
Zhang, Yu-Dong
author_facet Rehman, Shams ur
Khan, Muhamamd Attique
Masood, Anum
Almujally, Nouf Abdullah
Baili, Jamel
Alhaisoni, Majed
Tariq, Usman
Zhang, Yu-Dong
author_sort Rehman, Shams ur
collection PubMed
description The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.
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spelling pubmed-101786342023-05-13 BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images Rehman, Shams ur Khan, Muhamamd Attique Masood, Anum Almujally, Nouf Abdullah Baili, Jamel Alhaisoni, Majed Tariq, Usman Zhang, Yu-Dong Diagnostics (Basel) Article The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained. MDPI 2023-05-03 /pmc/articles/PMC10178634/ /pubmed/37175009 http://dx.doi.org/10.3390/diagnostics13091618 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rehman, Shams ur
Khan, Muhamamd Attique
Masood, Anum
Almujally, Nouf Abdullah
Baili, Jamel
Alhaisoni, Majed
Tariq, Usman
Zhang, Yu-Dong
BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title_full BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title_fullStr BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title_full_unstemmed BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title_short BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images
title_sort brmi-net: deep learning features and flower pollination-controlled regula falsi-based feature selection framework for breast cancer recognition in mammography images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178634/
https://www.ncbi.nlm.nih.gov/pubmed/37175009
http://dx.doi.org/10.3390/diagnostics13091618
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