ArticleComputational and mathematical methods in medicine2021
Bone Cancer Detection Using Feature Extraction Based Machine Learning Model.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 155 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.Journal of imaging informatics in medicine · 2024Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- ROI-guided relational YOLO-SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images.Scientific reports · 2026Article
- Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays.Scientific reports · 2026Article
- Bone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- Vision Transformer Autoencoders for Unsupervised Representation Learning: Revealing Novel Genetic Associations through Learned Sparse Attention Patterns.medRxiv : the preprint server for health sciences · 2025Article
- A comprehensive deep learning approach to improve enchondroma detection on X-ray images.Scientific reports · 2025Article
- Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images.Frontiers in medicine · 2025Article
- Diagnosis based image quality assessment and enhancement for low dose CT image.Frontiers in radiology · 2025Article
- A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.Scientific reports · 2024Article
- Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm.Scientific reports · 2023Article
- DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction.Diagnostics (Basel, Switzerland) · 2023Article
- Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models.Diagnostics (Basel, Switzerland) · 2022Article
- Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers.BMC bioinformatics · 2022Article
- Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bone cancer is considered a serious health problem, and, in many cases, it causes patient death. The X-ray, MRI, or CT-scan image is used by doctors to identify bone cancer. The manual process is time-consuming and required expertise in that field. Therefore, it is necessary to develop an automated system to classify and identify the cancerous bone and the healthy bone. The texture of a cancer bone is different compared to a healthy bone in the affected region. But in the dataset, several images of cancer and healthy bone are having similar morphological characteristics. This makes it difficult to categorize them. To tackle this problem, we first find the best suitable edge detection algorithm after that two feature sets one with hog and another without hog are prepared. To test the efficiency of these feature sets, two machine learning models, support vector machine (SVM) and the Random forest, are utilized. The features set with hog perform considerably better on these models. Also, the SVM model trained with hog feature set provides an
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.