Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography DOI Creative Commons
Alessandro Stefano, Fabiano Bini,

Eleonora Giovagnoli

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(8), P. 953 - 953

Published: April 9, 2025

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% cases. Early diagnosis, based on identification radiological features, such as masses and microcalcifications in mammograms, crucial reducing rates. However, manual interpretation by radiologists complex subject to variability, emphasizing need automated diagnostic tools enhance accuracy efficiency. This study compares a radiomics workflow machine learning (ML) with deep (DL) approach classifying breast lesions benign or malignant. Methods: matRadiomics was used extract features from mammographic images 1219 patients CBIS-DDSM public database, including 581 cases 638 masses. Among ML models, linear discriminant analysis (LDA) demonstrated best performance both lesion types. External validation conducted private dataset 222 evaluate generalizability an independent cohort. Additionally, EfficientNetB6 model employed comparison. Results: The LDA achieved mean AUC 68.28% 61.53% In external validation, values 66.9% 61.5% were obtained, respectively. contrast, superior performance, achieving 81.52% 76.24% masses, highlighting potential DL improved accuracy. Conclusions: underscores limitations ML-based diagnosis. Deep proves be more effective approach, offering enhanced supporting clinicians improving patient management.

Language: Английский

Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends DOI

Mutaz Abdel Wahed,

Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon

et al.

LatIA, Journal Year: 2024, Volume and Issue: 3, P. 117 - 117

Published: Oct. 18, 2024

Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews AI applications this field often lack cohesion, with each study adopting a unique approach. The aim is to provide detailed examination AI's role diagnosis through citation analysis, helping categorize key areas that attract academic attention. It also includes thematic analysis identify specific research topics within category. A total 30,200 related and AI, published between 2015 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, Google Scholar. After applying inclusion exclusion criteria, 32 relevant identified. Most these utilized classification models for prediction, high accuracy being most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged preferred model many studies. findings indicate both quantity quality AI-based algorithms are increases given years. increasingly seen complement healthcare sector clinical expertise, target enhancing accessibility affordability worldwide.

Language: Английский

Citations

24
Enhancing Diagnostic Efficiency: A Radiomics Approach for Distinguishing Benign and Malignant Breast Lesions Using BI-RADS Features from Ultrasound Imaging DOI

Runqiu Cai,

Man Wang, Yan Yu

et al.

Clinical Breast Cancer, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography DOI Creative Commons
Alessandro Stefano, Fabiano Bini,

Eleonora Giovagnoli

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(8), P. 953 - 953

Published: April 9, 2025

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% cases. Early diagnosis, based on identification radiological features, such as masses and microcalcifications in mammograms, crucial reducing rates. However, manual interpretation by radiologists complex subject to variability, emphasizing need automated diagnostic tools enhance accuracy efficiency. This study compares a radiomics workflow machine learning (ML) with deep (DL) approach classifying breast lesions benign or malignant. Methods: matRadiomics was used extract features from mammographic images 1219 patients CBIS-DDSM public database, including 581 cases 638 masses. Among ML models, linear discriminant analysis (LDA) demonstrated best performance both lesion types. External validation conducted private dataset 222 evaluate generalizability an independent cohort. Additionally, EfficientNetB6 model employed comparison. Results: The LDA achieved mean AUC 68.28% 61.53% In external validation, values 66.9% 61.5% were obtained, respectively. contrast, superior performance, achieving 81.52% 76.24% masses, highlighting potential DL improved accuracy. Conclusions: underscores limitations ML-based diagnosis. Deep proves be more effective approach, offering enhanced supporting clinicians improving patient management.

Language: Английский

Citations

0