Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1785040911518924800 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10178634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rehmanshamsur brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT khanmuhamamdattique brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT masoodanum brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT almujallynoufabdullah brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT bailijamel brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT alhaisonimajed brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT tariqusman brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages AT zhangyudong brminetdeeplearningfeaturesandflowerpollinationcontrolledregulafalsibasedfeatureselectionframeworkforbreastcancerrecognitioninmammographyimages |