Enhancing Breast Cancer Detection: A Hybrid Approach Integrating Local Binary Pattern Features and Deep Learning Insights from Mammogram Images
DOI:
https://doi.org/10.22399/ijcesen.1526Keywords:
Breast cancer detection, Mammogram images, Local binary pattern, VGG-19, SVM, KNNAbstract
Early identification of breast cancer improves treatment outcomes and lowers mortality rates. Mammogram images are useful for diagnosis, but their interpretation can be difficult and time-consuming. The current study analyzes the feasibility of promoting handmade and deep learning features to enhance the accuracy of breast cancer identification using mammography pictures. Previously, manual feature extraction has been labor-intensive and inconsistent. Furthermore, deep learning systems frequently suffer from limited data and architectural inefficiencies. To overcome these problems, we provide a novel strategy that makes use of both local binary pattern (LBP) features and automatic feature extraction from seven deep learning models. The concatenated LBP97.5%, and SVM and KNN classifiers trained on the hybrid feature beat existing state-of-the-art models. Our findings indicate the usefulness of this hybrid feature technique. This work demonstrates the potential of the suggested feature extraction strategy in improving classifier performance for breast cancer identification from mammography images. Our technique shows promise for early and more accurate diagnosis, contributing to better patient outcomes in the fight against breast cancer
References
Patel, A., et al. (2020). Deep learning with transfer learning. Journal of Medical Imaging. 10(3);123-135.
Zhou, X., et al. (2020). GAN-based image augmentation for breast cancer detection. International Conference on Computer Vision. 45(6);789-801.
Anderson, R., et al. (2020). Ensemble of CNN and RF for mammogram analysis. Medical Image Analysis. 30(8);1123-1135.
Johnson, S., et al. (2021). Hybrid LBP and deep learning approach for breast cancer detection. IEEE Transactions on Medical Imaging. 40(2);256-268.
Lee, H., et al. (2020). Transfer learning with CNN for mammography analysis. Journal of Healthcare Engineering. 15(4);567-580.
Zhang, L., et al. (2020). Ensemble of CNN and SVM for breast cancer diagnosis. Proceedings of the International Conference on Medical Image Computing. 78(5);1021-1034.
Chen, J., et al. (2020). Deep CNN with attention mechanism for breast cancer classification. IEEE Journal of Biomedical and Health Informatics. 25(7);910-922.
Wang, Y., et al. (2021). Transfer learning and ensemble approach for breast cancer detection. Medical Physics. 35(9);1789-1802.
Yang, Q., et al. (2020). Transfer learning with GAN-generated data for synthetic mammogram analysis. Computer Methods and Programs in Biomedicine. 28(11);1456-1468.
Kim, S., et al. (2021). Deep CNN with attention and LBP fusion for breast cancer classification. International Conference on Image Processing. 22(4);789-801.
Zheng, L., et al. (2021). Hybrid CNN and SVM with data augmentation for breast cancer detection. Computerized Medical Imaging and Graphics. 18(6);1234-1247.
Zhang, X., et al. (2020). Fusion of handcrafted and deep features for breast cancer diagnosis. IEEE Transactions on Biomedical Engineering. 48(7);987-999.
S. Balakrishnan, N.S. Simonthomas, J. Nithyashri, B. Suchitra, G. Umamaheswari, & D. Pradeep. (2025). Automated Diagnosis of Cancer Disease with Human Tissues using Haralick Texture Features and Deep Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.904
M. Venkata Ramana, P.N. Jyothi, S.Anuradha, & G. Lakshmeeswari. (2025). Enhanced Bone Cancer Diagnosis through Deep Learning on Medical Imagery. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.931
Bandla Raghuramaiah, & Suresh Chittineni. (2025). BreastHybridNet: A Hybrid Deep Learning Framework for Breast Cancer Diagnosis Using Mammogram Images. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.812
Shajeni Justin, & Tamil Selvan. (2025). A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.788
Bandla Raghuramaiah, & Suresh Chittineni. (2025). BCDNet: An Enhanced Convolutional Neural Network in Breast Cancer Detection Using Mammogram Images. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.811
Bolleddu Devananda Rao, & K. Madhavi. (2024). BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.430
R. Ilakkiya, T. Jayakumar, S. Sujitha, & E. Vargees Kaviyan. (2025). Stability Analysis and Numerical Approach to Chemotherapy Model for the Treatment of Lung Cancer. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1095
Vural, M., Kabaca, A., Aksoy, S. H., Demir, M., Karaçam, S. Çavdar, Ulusoy, İdil, … Günay, O. (2025). Determination Of Radiation Dose Levels to Which Partois And Spinal Cord (C1-C2) Regions Are Exposed In Computed Tomography Brain Imaging. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.17
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
