Revolutionizing Cancer Detection with Machine Learning and Real-Time Reporting

DOI

10.2991/978-94-6463-738-0_46How to use a DOI?

Keywords

Early detection; healthcare diagnostics; machine learning; cancer detection; convolutional neural networks

Abstract

The rising incidence of cancer globally highlights the urgent need for more accurate and early detection methods. This research initiative proposes a dual approach: first, the development of a state-of-the-art machine learning model trained to identify cancerous cells with high precision; second, the implementation of a user-friendly interface designed for healthcare professionals to easily access and interpret results. At the core of this system is a highly trained Convolutional Neural Network tailored for cancer detection. The accompanying web interface will facilitate seamless reporting and provide healthcare practitioners with real-time updates on patient diagnostics. The suggested method performed exceptionally well, obtaining high accuracy in differentiating between benign and malignant tissues after extensive training on a variety of medical imaging datasets. Further improving the system’s accuracy and adaptability in clinical settings was the addition of an iterative feedback mechanism. This comprehensive approach—merging the power of deep learning with an accessible reporting mechanism—aimes to revolutionize cancer diagnostics, paving the way for more effective and timely treatment interventions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Gautam Kumar
AU  - Arpit
AU  - Akshit Bansal
AU  - Mohit Gupta
PY  - 2025
DA  - 2025/06/22
TI  - Revolutionizing Cancer Detection with Machine Learning and Real-Time Reporting
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 574
EP  - 585
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_46
DO  - 10.2991/978-94-6463-738-0_46
ID  - Kumar2025
ER  -