AI-Driven Integration of CNN and R-CNN for Improved Osteosarcoma Detection and Enhanced Prediction Accuracy

DOI

10.2991/978-94-6463-738-0_44How to use a DOI?

Keywords

Osteosarcoma; Medical image analysis; Convolutional Neural Network (CNN); Region-based Convolutional Neural Network (R-CNN); Cancer detection

Abstract

Metastatic osteosarcoma is the most common form of bone cancer in children and has a poor prognosis, particularly at advanced stages. Early detection is necessary to minimize the ability of malignant cells to spread and to reduce death rates. Despite various factors, such as bone tissue damage, degradation, infection, and delayed recuperation due to ordinary stimulating tumor growth, early-stage prognosis and prevention remain key to mitigating its effects. To cope with these demanding situations, we suggest a unique deep getting-to-know set of rules that integrates convolutional neural networks (CNNs) and region-based convolutional neural networks (R-CNNs) to beautify osteosarcoma detection from medical images. The CNNs are used to extract high-strength capabilities, while the R-CNNs are employed to concentrate on areas of interest for tumor detection and classification. We aim to combine these tactics to increase the sensitivity and specificity of osteosarcoma detection by efficiently identifying and enlarging areas of interest. The experimental results show the superiority of our gadget in comparison to traditional strategies. AI-based graph-era techniques were employed to visualize the overall performance metrics, including precision, consideration, and F1-score. In addition, the artificial intelligence precision age confirmed the model’s performance, such as having above 96% detection accuracy, which greatly excels any of the traditional methods. Its ability to localize and classify malignant areas with greater precision opens a bright path for revolutionizing the diagnosis of osteosarcoma and improving patient results in time.

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  - J. Jayasri
AU  - V. Kaviarasi
AU  - C. Manimegalai
AU  - A. Inbavalli
PY  - 2025
DA  - 2025/06/22
TI  - AI-Driven Integration of CNN and R-CNN for Improved Osteosarcoma Detection and Enhanced Prediction Accuracy
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 552
EP  - 564
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_44
DO  - 10.2991/978-94-6463-738-0_44
ID  - Jayasri2025
ER  -