Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning

Title

Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning

Creator

Manjunatha Swamy, C.; Babu Kumar, S.; Kiran, B.; Vijay Kumar, M.S.

Description

Breast cancer remains a major global health challenge, with the complexity of managing diverse diagnostic tests often hindering timely and accurate detection. This system proposes a solution by unifying various test results, such as imaging, biopsy and genetic data, into a single platform that leverages machine learning (ML) to predict the likelihood of breast cancer. The platform features an intuitive dashboard that visually represents deviations from normal values, enabling healthcare providers to make informed decisions for early detection and treatment planning. In addition, the system includes an interactive chatbot powered by natural language processing, which assists both doctors and patients by interpreting test results, explaining predictions and offering real-time suggestions for treatment options. This comprehensive approach not only integrates ML models to enhance diagnostic accuracy but also provides real-time updates and alerts for critical changes in patient data. By consolidating fragmented information and incorporating predictive analytics, the system aims to improve the precision of cancer monitoring and offer personalized treatment guidance. About 98% early detection accuracy is achieved to do decision-making processes better which leads to efficient treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1371 LNNS;pp.473-484

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Breast cancer; Cancer prediction model; Convolutional neural networks (CNN); Machine learning; Machine learning in healthcare; Medical image analysis

Coverage

Manjunatha Swamy C., Christ University, Bangalore, India; Babu Kumar S., Christ University, Bangalore, India; Kiran B., PES University, Bangalore, India; Vijay Kumar M.S., MIT, Mysore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981965722-3;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-5723-0_35

https://www.scopus.com/pages/publications/105018915958?origin=resultslist