International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning-Driven Strategies Radiotherapy Treatment Planning |
|---|---|
| ABSTRACT | Designing radiation treatments tailored to each patient is a major challenge in the fight against cancer. Traditional methods often depend heavily on human judgment and general rules, which can lead to less effective results and unwanted side effects. This research explores how machine learning can help plan radiation therapy by using patient-specific anatomical and Tumor-specific data. The main aim is to predict the correct radiation doses, locate Tumor areas accurately, and protect healthy tissues. To achieve this, the study uses techniques like Deep Learning (DL), Support Vector Machine (SVM), and Random Forest (RF) models. For machine learning to be effective in radiotherapy planning, it is important to focus on managing diverse types of data, making models easy to understand, and validating results clinically. By applying machine learning, it is possible to improve the accuracy and effectiveness of radiation treatments, leading to better patient outcomes and quality of life. |
| AUTHOR | DR. T. V. S SRIRAM, S. JAYAPRADHA, B.HARINI Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407026 |
| pdf/26_Machine Learning-Driven Strategies Radiotherapy Treatment Planning.pdf | |
| KEYWORDS | |
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