A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells

Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection m...

Full description

Saved in:
Bibliographic Details
Published inICT express Vol. 7; no. 4; pp. 440 - 444
Main Authors M., Sreeraj, Joy, Jestin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2021
Elsevier
한국통신학회
Subjects
Breast cancer
Deep learning
Machine learning
NAS scoring
전자/정보통신공학
Deep learning
NAS scoring
Breast cancer
Machine learning
Online AccessGet full text

Cover

More Information
Summary:Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection method works by converting each and every cancerous tissue into objects. Along with detecting the grade, proposed mechanism gives the count of the detected cells. This assists pathologists in identifying whether cells are cancerous or not along with the count of each type. Proposed model was evaluated on MITOS-ATYPIA-14 Challenge dataset. Accuracy of 0.89 and precision of 0.87 is obtained by the best method. Results indicate that the proposed machine learning technique has better performance as compared to existing methods and can aid pathologists in the detection process.
ISSN:2405-9595
2405-9595
DOI:10.1016/j.icte.2021.02.005