Enhancing Breast Cancer Detection: A Hybrid Approach Integrating Local Binary Pattern Features and Deep Learning Insights from Mammogram Images DOI Open Access

D. Sujitha Priya,

V. Radha

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 13, 2025

Early identification of breast cancer improves treatment outcomes and lowers mortality rates. Mammogram images are useful for diagnosis, but their interpretation can be difficult time-consuming. The current study analyzes the feasibility promoting handmade deep learning features to enhance accuracy using mammography pictures. Previously, manual feature extraction has been labor-intensive inconsistent. Furthermore, systems frequently suffer from limited data architectural inefficiencies. To overcome these problems, we provide a novel strategy that makes use both local binary pattern (LBP) automatic seven models. concatenated LBP97.5%, SVM KNN classifiers trained on hybrid beat existing state-of-the-art Our findings indicate usefulness this technique. This work demonstrates potential suggested in improving classifier performance images. technique shows promise early more accurate contributing better patient fight against

Language: Английский

Enhancing Breast Cancer Detection: A Hybrid Approach Integrating Local Binary Pattern Features and Deep Learning Insights from Mammogram Images DOI Open Access

D. Sujitha Priya,

V. Radha

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 13, 2025

Early identification of breast cancer improves treatment outcomes and lowers mortality rates. Mammogram images are useful for diagnosis, but their interpretation can be difficult time-consuming. The current study analyzes the feasibility promoting handmade deep learning features to enhance accuracy using mammography pictures. Previously, manual feature extraction has been labor-intensive inconsistent. Furthermore, systems frequently suffer from limited data architectural inefficiencies. To overcome these problems, we provide a novel strategy that makes use both local binary pattern (LBP) automatic seven models. concatenated LBP97.5%, SVM KNN classifiers trained on hybrid beat existing state-of-the-art Our findings indicate usefulness this technique. This work demonstrates potential suggested in improving classifier performance images. technique shows promise early more accurate contributing better patient fight against

Language: Английский

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