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Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers

Early detection of breast cancer through mammography is vital, with calcifications in mammograms serving as key indicators. Distinguishing between benign and malignant calcifications is essential for accurate diagnosis and treatment. This study aims to develop a Computer-Aided Detection (CAD) system...

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Main Author: Ham, Fatina Ham Yahya
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2024
Subjects:
R Medicine
RC254-282 Neoplasms. Tumors. Oncology (including Cancer)
Online Access:http://eprints.usm.my/61346/1/Fatina%20Ham%20Yahya%20Ham-E.pdf
http://eprints.usm.my/61346/
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spelling my.usm.eprints.61346 http://eprints.usm.my/61346/ Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers Ham, Fatina Ham Yahya R Medicine RC254-282 Neoplasms. Tumors. Oncology (including Cancer) Early detection of breast cancer through mammography is vital, with calcifications in mammograms serving as key indicators. Distinguishing between benign and malignant calcifications is essential for accurate diagnosis and treatment. This study aims to develop a Computer-Aided Detection (CAD) system to identify and classify breast calcifications. Data from confirmed breast cancer cases were collected from the Laboratory Information System (LIS) at the Women Imaging Suite (WISH) of Hospital Universiti Sains Malaysia (HUSM) and cross-verified with the Picture Archiving and Communication System (PACS) to select mammograms showing calcifications that met the inclusion criteria from September 2020 to December 2023. The performance of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models was evaluated using metrics such as accuracy, F1 score, recall, precision, specificity, sensitivity, and area under the curve (AUC). The SVM model showed balanced performance with 65.22% accuracy and an F1 score of 0.6, indicating a trade-off between precision (54.55%) and recall (66.67%). The KNN model had the lowest performance with 47.83% accuracy and an F1 score of 0.4, highlighting classification challenges. The RF model performed moderately with 60.87% accuracy and an F1 score of 0.47, showing high specificity (71.43%) but lower sensitivity (44.44%). Achieving 95% accuracy remains difficult due to reliance on high pixel value detection, limited complexity of machine learning models, and data constraints. Enhancing feature extraction, data augmentation, and model optimization could improve accuracy. Combining machine learning with deep learning or using ensemble methods offers promise for better classification, ultimately improving patient management. Universiti Sains Malaysia 2024-06 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/61346/1/Fatina%20Ham%20Yahya%20Ham-E.pdf Ham, Fatina Ham Yahya (2024) Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers. Project Report. Universiti Sains Malaysia. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic R Medicine
RC254-282 Neoplasms. Tumors. Oncology (including Cancer)
spellingShingle R Medicine
RC254-282 Neoplasms. Tumors. Oncology (including Cancer)
Ham, Fatina Ham Yahya
Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
description Early detection of breast cancer through mammography is vital, with calcifications in mammograms serving as key indicators. Distinguishing between benign and malignant calcifications is essential for accurate diagnosis and treatment. This study aims to develop a Computer-Aided Detection (CAD) system to identify and classify breast calcifications. Data from confirmed breast cancer cases were collected from the Laboratory Information System (LIS) at the Women Imaging Suite (WISH) of Hospital Universiti Sains Malaysia (HUSM) and cross-verified with the Picture Archiving and Communication System (PACS) to select mammograms showing calcifications that met the inclusion criteria from September 2020 to December 2023. The performance of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models was evaluated using metrics such as accuracy, F1 score, recall, precision, specificity, sensitivity, and area under the curve (AUC). The SVM model showed balanced performance with 65.22% accuracy and an F1 score of 0.6, indicating a trade-off between precision (54.55%) and recall (66.67%). The KNN model had the lowest performance with 47.83% accuracy and an F1 score of 0.4, highlighting classification challenges. The RF model performed moderately with 60.87% accuracy and an F1 score of 0.47, showing high specificity (71.43%) but lower sensitivity (44.44%). Achieving 95% accuracy remains difficult due to reliance on high pixel value detection, limited complexity of machine learning models, and data constraints. Enhancing feature extraction, data augmentation, and model optimization could improve accuracy. Combining machine learning with deep learning or using ensemble methods offers promise for better classification, ultimately improving patient management.
format Monograph
author Ham, Fatina Ham Yahya
author_facet Ham, Fatina Ham Yahya
author_sort Ham, Fatina Ham Yahya
title Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
title_short Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
title_full Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
title_fullStr Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
title_full_unstemmed Automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
title_sort automatic detection of calcifications in breast cancer diagnosis based on machine learning classifiers
publisher Universiti Sains Malaysia
publishDate 2024
url http://eprints.usm.my/61346/1/Fatina%20Ham%20Yahya%20Ham-E.pdf
http://eprints.usm.my/61346/
_version_ 1816131401338060800
score 13.244413

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