| Autor: |
Ahmad, Syed Shabbeer, Tejaswi, Satepuri, Latha, S. Bhargavi, Kumari, D. Suguna, Prasad, Srisailapu D. Vara, Bethu, Srikanth |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2938 Issue 1, p1-12, 12p |
| Abstrakt: |
Mitosis detection is one of the toughest things of cancer prognosis, sporting rapid diagnostic records that are required for most breast cancers grading. It offers critical clues to evaluate the fierceness of a tumor. The manual mitosis measure from a complete file of snapshots is a hard project. The purpose of this assignment is to advocate a supervised deep-learning version to come across mitosis signatures from breast histopathology MRI photos. The version may be based on a deep learning framework. It will likely be designed using a deep mastering structure with handcrafted features. The handcrafted capabilities consisting of morphological, textural, and depth features can be explored. The proposed structure ambitions to enhance precision, do not forget, and F-score. The proposed version will be very beneficial in a habitual examination, presenting pathologists with efficient opinion for breast most cancers grading from Magnetic Resonance photographs. The proposed model should lead pathologists, as medical researchers, to a superior know-how and assessment of breast cancer stage and genesis. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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