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Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.

Ramesh Ramamoorthy1, R S Shanmugasundaram1, A Athiraja2

  • 1Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, Tamilnadu, India.

Scientific Reports
|April 24, 2026
PubMed
Summary

This study introduces TriMedNet, a novel hybrid framework for bone cancer classification using multi-modal data. Combining imaging, clinical notes, and patient metrics, it achieves high diagnostic accuracy, improving early detection and treatment outcomes.

Keywords:
Bone Cancer DetectionClinical Text MiningConvolutional Neural Networks (CNN)Deep LearningMRI Image ClassificationMedical Data FusionMulti-modal DiagnosisRadiomics AnalysisTransformer Models (BERT)TriMedNet Architecture

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Bone cancer diagnosis presents significant healthcare challenges, necessitating accurate and early detection for effective treatment.
  • Current diagnostic methods often rely on single data types, potentially limiting accuracy.

Purpose of the Study:

  • To develop and evaluate TriMedNet, a novel hybrid framework for bone cancer classification.
  • To investigate the efficacy of integrating multi-modal data sources for improved diagnostic performance.

Main Methods:

  • TriMedNet integrates Convolutional Neural Network (CNN) for MRI analysis, Bidirectional Encoder Representations from Transformers (BERT) for clinical notes, and dense layers for patient metrics.
  • Features from these specialized branches are fused for final tumor classification.
  • The framework was trained and validated on the Roboflow dataset, incorporating biopsy and blood test results.

Main Results:

  • TriMedNet achieved a diagnostic accuracy of 98.5%, precision of 97.6%, and recall of 98.2%.
  • The study confirmed that fusing multi-modal features significantly enhances diagnostic performance compared to single-modality approaches.

Conclusions:

  • The TriMedNet framework demonstrates high accuracy and effectiveness in bone cancer diagnosis.
  • Multi-modal data integration offers a promising avenue for advancing bone cancer detection and supporting clinical decision-making.