Predicting breast cancer recurrence using deep learning
Abstract Breast cancer and its recurrence are significant health concerns, emphasizing the critical importance of early detection and personalized treatment strategies for improved outcomes. This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel ap...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06512-5 |
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| author | Deepa Kumari Mutyala Venkata Sai Subhash Naidu Subhrakanta Panda Jabez Christopher |
| author_facet | Deepa Kumari Mutyala Venkata Sai Subhash Naidu Subhrakanta Panda Jabez Christopher |
| author_sort | Deepa Kumari |
| collection | DOAJ |
| description | Abstract Breast cancer and its recurrence are significant health concerns, emphasizing the critical importance of early detection and personalized treatment strategies for improved outcomes. This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel approach designed to predict breast cancer recurrence with high accuracy and interpretability. Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). This hybridization leads to the creation of 16 robust models that enhance interpretability, facilitate generalization, and effectively manage challenges related to small datasets, class imbalance, and data preprocessing. The BCR-HDL framework’s unique contributions include its ability to predict not only diagnostic outcomes but also prognostic and recurrence timing, offering a comprehensive solution for breast cancer management. Specifically, the Hybrid MLP+RF and Xception+RF models achieved an exceptional diagnostic accuracy of 97% on the WDBC dataset, while the Hybrid MLP+RF model reached 78% prognostic accuracy on the WPBC dataset. Moreover, the Hybrid ResNet+SVM and ResNet+RF models demonstrated impressive performance in multi-classifying recurrence into different time intervals, achieving 92% accuracy in predicting recurrence within 2 years, between 2 to 4 years, and beyond 4 years. The study also provides a detailed analysis of model performance through training versus validation accuracy graphs and a comparison with existing approaches, demonstrating the superiority of the proposed framework in terms of diagnostic, prognostic, and recurrence time predictions. The BCR-HDL framework offers practical recommendations for clinicians, including its potential for personalized treatment strategies and improved patient monitoring, making it a valuable tool for advancing breast cancer management. |
| format | Article |
| id | doaj-art-c43b96191e6b4a7fa96eefc2ce42003b |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c43b96191e6b4a7fa96eefc2ce42003b2025-02-02T12:36:47ZengSpringerDiscover Applied Sciences3004-92612025-01-017213310.1007/s42452-025-06512-5Predicting breast cancer recurrence using deep learningDeepa Kumari0Mutyala Venkata Sai Subhash Naidu1Subhrakanta Panda2Jabez Christopher3CSIS Department, BITS Pilani, Hyderabad CampusCSIS Department, BITS Pilani, Hyderabad CampusCSIS Department, BITS Pilani, Hyderabad CampusCSIS Department, BITS Pilani, Hyderabad CampusAbstract Breast cancer and its recurrence are significant health concerns, emphasizing the critical importance of early detection and personalized treatment strategies for improved outcomes. This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel approach designed to predict breast cancer recurrence with high accuracy and interpretability. Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). This hybridization leads to the creation of 16 robust models that enhance interpretability, facilitate generalization, and effectively manage challenges related to small datasets, class imbalance, and data preprocessing. The BCR-HDL framework’s unique contributions include its ability to predict not only diagnostic outcomes but also prognostic and recurrence timing, offering a comprehensive solution for breast cancer management. Specifically, the Hybrid MLP+RF and Xception+RF models achieved an exceptional diagnostic accuracy of 97% on the WDBC dataset, while the Hybrid MLP+RF model reached 78% prognostic accuracy on the WPBC dataset. Moreover, the Hybrid ResNet+SVM and ResNet+RF models demonstrated impressive performance in multi-classifying recurrence into different time intervals, achieving 92% accuracy in predicting recurrence within 2 years, between 2 to 4 years, and beyond 4 years. The study also provides a detailed analysis of model performance through training versus validation accuracy graphs and a comparison with existing approaches, demonstrating the superiority of the proposed framework in terms of diagnostic, prognostic, and recurrence time predictions. The BCR-HDL framework offers practical recommendations for clinicians, including its potential for personalized treatment strategies and improved patient monitoring, making it a valuable tool for advancing breast cancer management.https://doi.org/10.1007/s42452-025-06512-5Breast cancer recurrenceDeep learningMachine learningHybrid modelFeature engineering |
| spellingShingle | Deepa Kumari Mutyala Venkata Sai Subhash Naidu Subhrakanta Panda Jabez Christopher Predicting breast cancer recurrence using deep learning Discover Applied Sciences Breast cancer recurrence Deep learning Machine learning Hybrid model Feature engineering |
| title | Predicting breast cancer recurrence using deep learning |
| title_full | Predicting breast cancer recurrence using deep learning |
| title_fullStr | Predicting breast cancer recurrence using deep learning |
| title_full_unstemmed | Predicting breast cancer recurrence using deep learning |
| title_short | Predicting breast cancer recurrence using deep learning |
| title_sort | predicting breast cancer recurrence using deep learning |
| topic | Breast cancer recurrence Deep learning Machine learning Hybrid model Feature engineering |
| url | https://doi.org/10.1007/s42452-025-06512-5 |
| work_keys_str_mv | AT deepakumari predictingbreastcancerrecurrenceusingdeeplearning AT mutyalavenkatasaisubhashnaidu predictingbreastcancerrecurrenceusingdeeplearning AT subhrakantapanda predictingbreastcancerrecurrenceusingdeeplearning AT jabezchristopher predictingbreastcancerrecurrenceusingdeeplearning |