التفاصيل البيبلوغرافية
| العنوان: |
Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer |
| المؤلفون: |
S. R. Sannasi Chakravarthy, N. Bharanidharan, V. Vinoth Kumar, T. R. Mahesh, Mohammed S. Alqahtani, Suresh Guluwadi |
| المصدر: |
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-15 (2024) |
| بيانات النشر: |
BMC, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Medical technology |
| مصطلحات موضوعية: |
Deep learning, Fuzzy ranking, Convolution neural network, Transfer learning, Medical technology, R855-855.5 |
| الوصف: |
Abstract Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1471-2342 |
| Relation: |
https://doaj.org/toc/1471-2342 |
| DOI: |
10.1186/s12880-024-01267-8 |
| URL الوصول: |
https://doaj.org/article/1507abf64dcd4f31a26eb8fbac756db2 |
| رقم الانضمام: |
edsdoj.1507abf64dcd4f31a26eb8fbac756db2 |
| قاعدة البيانات: |
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