A Confidence-Optimised and Edge-Guided Deep Learning Framework for Precise Lung Cancer Identification and Risk Assessment

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide due to challenges in early detection and accurate risk stratification. Recent advancements in deep learning have revolutionized medical imaging, enabling precise tumor detection, classification, and prognosis. However, conventional deep learning systems often lack interpretability, robustness, and confidence-aware prediction, making clinical adoption limited. This paper proposes a Confidence-Optimized and Edge-Guided Deep Learning Framework (COEG-DLF) for lung cancer identification and risk assessment. The framework integrates edge-preserving segmentation, convolutional feature extraction, and probabilistic confidence calibration to ensure robust tumor boundary delineation and reliable risk stratification. We incorporate attention-guided convolutional neural networks (CNNs) for high-level feature extraction and a Bayesian confidence optimization layer for uncertainty estimation. A large-scale survey of existing methods is presented to benchmark the strengths and limitations of prior models. Experimental evaluation using publicly available lung cancer datasets (LIDC-IDRI, TCIA) demonstrates that the proposed framework outperforms traditional CNN and transformer-based approaches in terms of accuracy, precision, and reliability of predictions. This work contributes to the field by introducing an interpretable, clinically reliable, and computationally efficient framework that supports oncologists in early detection and personalized treatment planning.

Introduction

Lung cancer is a leading cause of cancer-related deaths globally, primarily due to late diagnosis. Although CT imaging, especially Low-Dose CT (LDCT), is effective for early detection, interpreting these scans is challenging and error-prone. To address these limitations, artificial intelligence (AI), particularly deep learning (DL), has shown promise in automating lung nodule detection and classification. However, current DL models struggle with precise boundary detection, lack interpretability, and often give overconfident predictions.

To overcome these issues, this study proposes the Confidence-Optimized and Edge-Guided Deep Learning Framework (COEG-DLF), which integrates three key components:

1. Edge-Guided Segmentation Module: Enhances boundary precision using Sobel and Laplacian filters within a U-Net-like architecture.

2. Attention-Based CNN: Focuses on clinically relevant features using spatial and channel attention mechanisms.

3. Confidence-Optimized Bayesian Inference Layer: Provides calibrated uncertainty estimates using Monte Carlo dropout for more reliable and interpretable predictions.

The framework was trained and tested on large-scale CT datasets (LIDC-IDRI and TCIA), using advanced data preprocessing and augmentation techniques. Performance was evaluated using metrics such as accuracy, sensitivity, specificity, Dice coefficient, and Expected Calibration Error (ECE).

Compared to previous models—standard CNNs, U-Nets, and transformer-based models—COEG-DLF delivers improved segmentation accuracy, better interpretability, and more trustworthy predictions. It reduces false positives, supports clinical decision-making, and enables personalized lung cancer risk assessment.

Conclusion

Lung cancer remains a formidable global health challenge, responsible for more deaths than breast, colon, and prostate cancers combined. Despite advancements in screening technologies such as low-dose computed tomography (LDCT), the clinical community continues to grapple with challenges of early detection, accurate classification, and reliable risk stratification. The research presented in this study introduces a novel Confidence-Optimized and Edge-Guided Deep Learning Framework (COEG-DLF) designed to address these challenges by combining three critical elements: precise edge-aware segmentation, attention-guided feature extraction, and calibrated Bayesian inference for confidence optimization. The findings from this work underscore the fact that technical innovations in medical AI must extend beyond raw accuracy metrics. While many previous studies have demonstrated high sensitivity and specificity for lung nodule detection using convolutional neural networks (CNNs) (Shen et al., 2017; Setio et al., 2016), these models often lack interpretability and reliability, limiting their acceptance in real-world clinical workflows. COEG-DLF directly responds to these shortcomings by embedding interpretability and uncertainty estimation into its architecture, thereby enhancing trustworthiness — a quality increasingly recognized as essential for clinical AI deployment (Samek et al., 2017; Guo et al., 2017). The integration of an edge-guided segmentation module provides a significant improvement over traditional CNN and U-Net–based segmentation frameworks. By incorporating Sobel and Laplacian operators within convolutional layers, the framework successfully captures fine-grained tumor boundaries, even in cases where nodules are irregularly shaped, small, or attached to vascular structures. Accurate segmentation is not only a technical achievement but also has direct clinical implications: boundary precision is a critical factor for tumor size measurement, staging, and subsequent treatment planning (Ronneberger et al., 2015; Milletari et al., 2016). Mis-segmentation could lead to underestimation of tumor progression or unnecessary interventions; hence, improvements in this area carry tangible benefits for patient care. Perhaps the most innovative aspect of COEG-DLF lies in its confidence-optimized Bayesian inference layer, which directly addresses the issue of unreliable, overconfident predictions — a known limitation of conventional deep learning approaches. Unlike deterministic CNNs, which produce fixed probability scores, COEG-DLF leverages Monte Carlo dropout to approximate Bayesian inference, generating a distribution of predictions and quantifying uncertainty. This is particularly relevant in cases involving ground-glass opacities (GGOs) or nodules with atypical imaging features, where radiologists themselves often struggle to agree on malignancy potential (McWilliams et al., 2013). By providing uncertainty-aware predictions, COEG-DLF ensures that high-confidence predictions can be trusted, while ambiguous cases can be flagged for additional expert review. The alignment of model probabilities with actual outcomes, as evidenced by improved Expected Calibration Error (ECE), represents a crucial step toward clinically interpretable AI (Kendall & Gal, 2017). Another strength of the proposed framework is its capacity for personalized risk assessment, achieved by integrating imaging-derived features with clinical metadata such as patient age, smoking history, and comorbidities. This aligns with the broader paradigm shift in oncology toward precision medicine, where risk stratification and treatment strategies are tailored to individual patients rather than applied uniformly across populations (Collins & Varmus, 2015). By generating calibrated probability scores that reflect both imaging and non-imaging data, COEG-DLF enables clinicians to design more individualized follow-up protocols, potentially reducing both under- and over-treatment. Moreover, the system demonstrates potential to mitigate the persistent challenge of false positives in lung cancer screening. High false-positive rates have historically limited the adoption of LDCT screening, as seen in the National Lung Screening Trial (NLST, 2011), where nearly one in four patients experienced a false alarm. By incorporating uncertainty estimation, COEG-DLF reduces false positives and ensures that only predictions with sufficient confidence are escalated for invasive diagnostic procedures. This not only improves clinical efficiency but also reduces patient anxiety and unnecessary healthcare costs. When compared with existing models such as standard CNN-based classifiers, U-Net segmentation frameworks, and transformer-based architectures, COEG-DLF offers a balanced trade-off between accuracy, efficiency, and interpretability. Transformer-based models, while effective in learning global dependencies, remain data-hungry and computationally expensive, making them impractical in many clinical contexts (Dosovitskiy et al., 2020). CNN-based approaches, though lightweight, often fail in boundary precision and lack uncertainty estimation. COEG-DLF occupies a unique position by integrating the strengths of these models while mitigating their weaknesses. Despite its promising results, COEG-DLF is not without limitations. First, the model relies heavily on high-quality CT imaging datasets such as LIDC-IDRI and TCIA. While these datasets are comprehensive, they may not capture the full variability of imaging protocols in real-world clinical environments, particularly in low-resource settings where image quality may be compromised. This raises concerns about model generalizability across diverse populations and scanners. Second, while the attention mechanisms and uncertainty estimation improve interpretability, the system still does not provide a fully human-understandable rationale for its predictions. Visualizations such as attention maps and uncertainty distributions offer partial insights, but additional explainable AI (XAI) methods, such as SHAP (SHapley Additive Explanations) values or counterfactual reasoning, could further enhance clinical transparency (Rudin, 2019). Finally, the computational requirements, though reduced compared to transformers, remain significant, necessitating access to high-performance GPUs for training and inference. This could limit adoption in smaller healthcare centers without advanced computational infrastructure. Building on the strengths of COEG-DLF, several promising avenues for future research can be identified. First, integrating multi-modal data sources, including genomic markers, histopathology images, and electronic health records, could enable a more comprehensive understanding of tumor biology and patient risk. Such integration would align with recent trends in radiogenomics, where imaging features are linked with molecular data for more accurate predictions of tumor behavior (Yip & Aerts, 2016). Second, extending the framework to incorporate longitudinal imaging data could allow dynamic monitoring of nodule growth over time, thereby improving the distinction between benign and malignant lesions. Growth rate has long been recognized as a key indicator of malignancy, and incorporating temporal patterns could significantly enhance predictive accuracy. Third, conducting prospective clinical trials will be critical for validating the framework in real-world practice. While retrospective evaluations on benchmark datasets provide valuable insights, prospective studies in hospital workflows will reveal the practical challenges and benefits of integrating COEG-DLF into existing diagnostic pipelines. Finally, efforts should be directed toward model compression and optimization to enable deployment in resource-constrained settings. Techniques such as pruning, quantization, and knowledge distillation could reduce computational overhead while preserving accuracy, thereby making the framework accessible to a broader range of healthcare institutions. In conclusion, the Confidence-Optimized and Edge-Guided Deep Learning Framework (COEG-DLF) represents a significant advancement in the application of artificial intelligence to lung cancer diagnosis. By addressing the interrelated challenges of precision, reliability, and interpretability, the framework bridges critical gaps between experimental AI models and clinically deployable systems. While limitations remain, the contributions of COEG-DLF to segmentation accuracy, uncertainty-aware risk assessment, and personalized patient care are substantial. As the field of medical AI continues to evolve, the integration of confidence-aware, interpretable, and clinically aligned frameworks such as COEG-DLF will be essential for realizing the full potential of AI in oncology. With continued refinement and clinical validation, such systems hold the promise of not only reducing lung cancer mortality but also reshaping the future of diagnostic radiology and personalized medicine.

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Copyright

Copyright © 2025 Mr. A. Balraj, Dr. J. Karunanithi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.