Breast cancer detection from histopathological images using machine learning models

L. N. Das; Sachin Saini; Puneet Kataria; Dipanshu

doi:10.53730/ijhs.v6nS3.8254

Breast cancer detection from histopathological images using machine learning models

https://doi.org/10.53730/ijhs.v6nS3.8254

Authors

L. N. Das Department of Applied Mathematics, Delhi Technological University
Sachin Saini Department of Applied Mathematics, Delhi Technological University
Puneet Kataria Department of Applied Mathematics, Delhi Technological University
Dipanshu
dipanshukumar_2k18mc034@dtu.ac.in
Department of Applied Mathematics, Delhi Technological University

Keywords:

breast cancer, histopathology, tumors, machine learning techniques

Abstract

Breast cancer is the most common cancer in women worldwide, accounting for more than 25% of all cancer cases and affecting more than 2.1 million people each year. According to the WHO, early detection is crucial to improving patient outcomes and survival. However, prognosis by histology of biopsy tissue is a complex procedure and the ultimate interpretation can be controversial. Therefore, machine learning algorithms are deployed to generate techniques that can be used by technicians, radiologists, and physicians as tools to unequivocally detect and diagnose breast cancer at an early stage. This will help to significantly increase the survival rate of the patients and their subsequent quality of life.

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References

Leo Breiman. 2001. Random forests. Machine learning 45(1):5–32.

Yali Amit and Donald Geman. 1997. Shape quantization and recognition with randomized trees. Neural computation 9(7):1545–1588

Borges et al. 2014. Benign and Malignant breast tumor classification based on region growing and CNN segmentation.

Lulu Wang 1. 2017. Early diagnosis of breast cancer.

Galotkar et al. 2017. New diagnostic tools for Breast Cancer.

Cakir and Demirel. 2011. A Software Tool for determination of Breast Cancer Treatment Methods.

Published

01-06-2022

How to Cite

Das, L. N., Saini, S., Kataria, P., & Dipanshu. (2022). Breast cancer detection from histopathological images using machine learning models. International Journal of Health Sciences, 6(S3), 9542–9553. https://doi.org/10.53730/ijhs.v6nS3.8254

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Issue

Vol. 6 No. S3 (2022)

Section

Peer Review Articles

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