Background: Panoramic dental radiographs frequently contain multiple coexisting pathological, anatomical, and treatment-related findings that vary in scale, contrast, and boundary definition. Although recent one-stage detectors provide strong performance, reliable localization of small or weakly defined findings remain...
Furkan Karataş, Ö. Aslan, Mehmet Burukanli et al.· Diagnostics· 0 citations
The results suggest that stacking-based ensembles can improve risk prediction from lifestyle and clinical indicators while maintaining model transparency through SHAP and LIME explanations, highlighting the potential of interpretable ensemble learning as a decision-support tool for lung cancer risk assessment.
Shahid Mohammad Ganie, P. K. Dutta Pramanik, Zhong-Ming Zhao· PLoS ONE· 0 citations
The proposed explainable ensemble learning framework provides accurate, robust, and interpretable lung cancer stage prediction through the integration of leakage-free model development, optimized ensemble learning, explainable artificial intelligence, statistical validation, and component-wise ablation analysis.
M. Alfuraydan, Shahid Mohammad Ganie, Ehab Seedahmed et al.· Discover Computing· 0 citations
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