Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning
Breast cancer; Cancer prediction model; Convolutional neural networks (CNN); Machine learning; Machine learning in healthcare; Medical image analysis
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.
Manjunatha Swamy, C.; Babu Kumar, S.; Kiran, B.; Vijay Kumar, M.S.
Lecture Notes in Networks and Systems;Volume;1371 LNNS;pp.473-484
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-5723-0_35 https://www.scopus.com/pages/publications/105018915958?origin=resultslist
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