1*Associate Professor, Dept. of Electronics and Communication Engineering, G. Narayanamma Institute of Technology and Science (for Women), Hyderabad, India. Email: c.padmaja@gnits.ac.in ORCID ID: 0000-0003-0521-916X
2Associate Professor, Dept. of Electrical and Electronics Engineering, Singapore Institute of Technology, Singapore. Email: sivaneasan@singaporetech.edu.sg ORCID ID: 0000-0002-4271-2677
3HOD and Professor, Dept. of Information Technology, G. Narayanamma Institute of Technology and Science (for Women), Hyderabad, India. Email: s.ramacharan@gnits.ac.in ORCID ID: 0000-0003-1058-2211
4Director, Senior Professor, Dept. of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India. Email: drprasun.cse@gmail.com ORCID ID: 0000-0001-8062-4144
Background: Breast cancer is the leading malignant disease in women globally, with 2.3 million new diagnoses and 685,000 deaths recorded annually. Clinical deployment of AI-based diagnostic tools has been impeded by model opacity, miscalibrated confidence, and unequal performance across patient subgroups.
Methods: We propose TEDIN (Trustworthy Explainable Deep Intelligence Network), a multi-module framework that couples a ResNet-50 transfer learning backbone with Gradient-weighted Class Activation Mapping (Grad-CAM) for spatial explainability, Monte Carlo (MC) Dropout for epistemic uncertainty quantification, and equalized-odds post-processing for demographic fairness correction. TEDIN is evaluated on the CBIS-DDSM mammography dataset (2568 images) and BreakHis histopathology dataset (7909 images).
Results: TEDIN achieves 97.4% accuracy, 96.8% sensitivity, 97.9% specificity, and AUC = 0.989, outperforming four state-of-the-art baselines. The MC-Dropout mechanism flags 88.2% of misclassified cases as high-uncertainty. Equalized-odds correction reduces cross-demographic false-negative rate disparities by up to 74.5% at less than 0.4 pp accuracy cost. A usability study with 12 radiologists confirms a 23% reduction in diagnostic review time and statistically significant gains in clinician confidence (p < 0.01).
Conclusions: TEDIN demonstrates that the reliability, interpretability, calibration, and fairness principles central to Trustworthy and Intelligent Systems for Predictive Maintenance transfer directly and productively into clinical breast cancer diagnosis.
Keywords: Explainable AI, Grad-CAM, Convolutional Neural Networks, TEDIN, Predictive maintenance
How to cite this article: Padmaja C, Bala Krishnan SS, Ramacharan S, Chakrabarti P. Next-Generation Radiogenomics: Advanced Diagnostics for Early Breast Cancer Detection and Personalized Risk Stratification. Int J Drug Deliv Technol. 2026;16(12s): 546-556. DOI: 10.25258/ijddt.16.12s.66
Source of support: Nil.
Conflict of interest: None