Smart Detection of Bone Cancer Using Machine Learning Techniques: A Comprehensive Review

Abstract

Bone cancer poses a serious threat to human life, necessitating timely and accurate diagnosis for effective treatment. Conventional diagnostic methods like MRIs, CT scans, and X-rays rely on medical personnel to manually interpret the results, which can be laborious and prone of human error. To overcome these obstacles, this work offers a machine learning-based automated approach for detecting and classifying bone cancer. Medical images are first pre-processed using a median filter to remove noise, followed by feature extraction utilizing a genetic algorithm and Convolutional Neural Network (CNN). The CNN classifier is then employed to analyze and categorize the images based on cancer stages. To enhance precision, advanced image processing methods like edge detection and clustering are incorporated. The system supports clinicians by improving diagnostic accuracy, enabling early intervention, and reducing unnecessary surgical procedures. Experimental findings reveal promising results with higher early detection rates, demonstrating the system\'s potential in real-world clinical applications.

Introduction

Cancer, particularly bone cancer like osteosarcoma and Ewing's sarcoma, poses a major global health challenge due to its aggressive nature and high mortality. In India alone, cancer affects millions, with increasing incidence and deaths expected worldwide by 2030. Traditional diagnosis relies on imaging techniques such as X-rays, CT, MRI, and PET scans, but manual interpretation by radiologists is time-consuming, error-prone, and affected by image quality issues.

To improve diagnostic accuracy and efficiency, automated image processing—especially image segmentation—is becoming essential. Segmentation isolates tumors from healthy tissue, aiding in precise measurement and classification. Deep learning techniques, notably convolutional neural networks (CNNs), have shown great promise in enhancing image clarity, tumor detection, and classification.

The study reviews various segmentation methods and identifies gaps, including insufficient focus on bone cancer, lack of hybrid models combining traditional and AI techniques, dataset limitations, and computational challenges hindering real-time clinical use. The proposed system uses machine learning to preprocess images, extract features, and classify bone tumors, aiming to reduce human error, speed up diagnosis, and improve treatment planning.

The system benefits include early detection, improved accuracy, cost-effectiveness, and standardization, with applications in medical diagnostics, oncology research, orthopedics, telemedicine, forensic studies, and biomedical engineering. Overall, integrating AI-based segmentation with medical imaging holds great potential for advancing bone cancer diagnosis and patient outcomes.

Conclusion

Machine learning & medical imaging methods for bone cancer detection have greatly increased the precision and effectiveness of diagnosis. Automated technologies that use MRI, CT, and X-rays improve early identification and classification for bone illnesses, while traditional diagnostic procedures are frequently laborious and prone to human mistake. Differentiating between healthy and malignant bones is made easier by methods involving image processing such feature extraction, differentiation, and classification. The integration of AI and deep learning models ensures precise and reliable outcomes, reducing misdiagnosis. This approach not only enhances patient care but also enables early intervention, improving survival rates. Further advancements in AI-driven medical imaging will continue to refine bone cancer detection, leading to better prognosis and treatment strategies.

References

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Copyright

Copyright © 2025 Mrs. Nitu Kumari, Prof. Nitin Shrivastava. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.