Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1784715015374241792 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9140096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT safdarsadia bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT rizwanmuhammad bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT gadekalluthippareddy bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT javedabdulrehman bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT rahmanimohammadkhalidimam bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT jawadkhurram bioimagingbasedmachinelearningalgorithmforbreastcancerdetection AT bhatiasurbhi bioimagingbasedmachinelearningalgorithmforbreastcancerdetection |