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Breast Cancer Detection and Prevention Using Machine Learning

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal...

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Autores principales: Khalid, Arslan, Mehmood, Arif, Alabrah, Amerah, Alkhamees, Bader Fahad, Amin, Farhan, AlSalman, Hussain, Choi, Gyu Sang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572157/
https://www.ncbi.nlm.nih.gov/pubmed/37835856
http://dx.doi.org/10.3390/diagnostics13193113
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author Khalid, Arslan
Mehmood, Arif
Alabrah, Amerah
Alkhamees, Bader Fahad
Amin, Farhan
AlSalman, Hussain
Choi, Gyu Sang
author_facet Khalid, Arslan
Mehmood, Arif
Alabrah, Amerah
Alkhamees, Bader Fahad
Amin, Farhan
AlSalman, Hussain
Choi, Gyu Sang
author_sort Khalid, Arslan
collection PubMed
description Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.
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spelling pubmed-105721572023-10-14 Breast Cancer Detection and Prevention Using Machine Learning Khalid, Arslan Mehmood, Arif Alabrah, Amerah Alkhamees, Bader Fahad Amin, Farhan AlSalman, Hussain Choi, Gyu Sang Diagnostics (Basel) Article Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. MDPI 2023-10-02 /pmc/articles/PMC10572157/ /pubmed/37835856 http://dx.doi.org/10.3390/diagnostics13193113 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
Khalid, Arslan
Mehmood, Arif
Alabrah, Amerah
Alkhamees, Bader Fahad
Amin, Farhan
AlSalman, Hussain
Choi, Gyu Sang
Breast Cancer Detection and Prevention Using Machine Learning
title Breast Cancer Detection and Prevention Using Machine Learning
title_full Breast Cancer Detection and Prevention Using Machine Learning
title_fullStr Breast Cancer Detection and Prevention Using Machine Learning
title_full_unstemmed Breast Cancer Detection and Prevention Using Machine Learning
title_short Breast Cancer Detection and Prevention Using Machine Learning
title_sort breast cancer detection and prevention using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572157/
https://www.ncbi.nlm.nih.gov/pubmed/37835856
http://dx.doi.org/10.3390/diagnostics13193113
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