Automatic Mitosis and Nuclear Atypia Detection for Breast Cancer Grading in Histopathological Images using Hybrid Machine Learning Technique.
Saved in:
| Title: | Automatic Mitosis and Nuclear Atypia Detection for Breast Cancer Grading in Histopathological Images using Hybrid Machine Learning Technique. |
|---|---|
| Authors: | Maheshwari, Neerudu Uma, SatheesKumaran, S. |
| Source: | Multimedia Tools & Applications; Dec2024, Vol. 83 Issue 42, p90105-90132, 28p |
| Subject Terms: | WOMEN'S mortality, FEATURE selection, MEDICAL sciences, OPTIMIZATION algorithms, ARTIFICIAL intelligence |
| Abstract: | Invasive breast cancer is a complex global health issue and the leading cause of women's mortality. Multiclassification in breast cancer, especially with high-resolution images, presents unique challenges. Clinical diagnosis relies on the cancer's pathological stage, requiring precise segmentation and adjustments. Complex structural changes during slide preparation and inconsistent image magnifications further complicate classification. To address these challenges, we propose a hybrid machine learning framework for accurate breast cancer detection and grading using large-scale pathological images. Our approach includes an improved Non-restricted Boltzmann Deep Belief Neural Network for nuclei segmentation, followed by feature extraction and novel feature selection using the Giraffe Kicking Optimization algorithm to mitigate overfitting. We implement an Optimal Kernel layer-based Support Vector Machine classifier to identify mitotic cells and nuclear atypia, using the Nottingham Grading System. Validation on the MITOSIS-ATYPIA-14 database demonstrates the framework's effectiveness, with performance metrics including accuracy, precision, recall, specificity, and F-measure. This approach addresses the complexities of breast cancer classification and grading in a streamlined manner, enhancing diagnostic accuracy and prognosis prediction. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!