ArticleScientific reports2024
A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- ROI-guided relational YOLO-SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images.Scientific reports · 2026Article
- Dynamic wavelet transform normalized multi scale dense attention UNet with Binary Horse Herd Optimization for osteosarcoma diagnosis.Scientific reports · 2026Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- A comprehensive deep learning approach to improve enchondroma detection on X-ray images.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.Journal of medical Internet research · 2025Article
- Advancing bone tumor detection in older adults: the impact of AI-enhanced medical imaging.Frontiers in medicine · 2025Review
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3 authors at 1 institution in 1 country.
Funding
Abstract
Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.
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