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Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection

Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing...

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Autores principales: Safdar, Sadia, Rizwan, Muhammad, Gadekallu, Thippa Reddy, Javed, Abdul Rehman, Rahmani, Mohammad Khalid Imam, Jawad, Khurram, Bhatia, Surbhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140096/
https://www.ncbi.nlm.nih.gov/pubmed/35626290
http://dx.doi.org/10.3390/diagnostics12051134
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author Safdar, Sadia
Rizwan, Muhammad
Gadekallu, Thippa Reddy
Javed, Abdul Rehman
Rahmani, Mohammad Khalid Imam
Jawad, Khurram
Bhatia, Surbhi
author_facet Safdar, Sadia
Rizwan, Muhammad
Gadekallu, Thippa Reddy
Javed, Abdul Rehman
Rahmani, Mohammad Khalid Imam
Jawad, Khurram
Bhatia, Surbhi
author_sort Safdar, Sadia
collection PubMed
description Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.
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spelling pubmed-91400962022-05-28 Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection Safdar, Sadia Rizwan, Muhammad Gadekallu, Thippa Reddy Javed, Abdul Rehman Rahmani, Mohammad Khalid Imam Jawad, Khurram Bhatia, Surbhi Diagnostics (Basel) Article Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection. MDPI 2022-05-03 /pmc/articles/PMC9140096/ /pubmed/35626290 http://dx.doi.org/10.3390/diagnostics12051134 Text en © 2022 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
Safdar, Sadia
Rizwan, Muhammad
Gadekallu, Thippa Reddy
Javed, Abdul Rehman
Rahmani, Mohammad Khalid Imam
Jawad, Khurram
Bhatia, Surbhi
Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title_full Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title_fullStr Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title_full_unstemmed Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title_short Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection
title_sort bio-imaging-based machine learning algorithm for breast cancer detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140096/
https://www.ncbi.nlm.nih.gov/pubmed/35626290
http://dx.doi.org/10.3390/diagnostics12051134
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