Abstract
Lung cancer remains one of the most serious causes of cancer-related mortality worldwide, mainly because it is frequently diagnosed at an advanced stage when therapeutic options are limited and survival outcomes are poor. Early and precise identification of malignant transformation is therefore essential for improving prognosis, enabling timely intervention, and supporting personalized treatment planning. Recent developments in nanotechnology, optical biosensing, artificial intelligence, and medical image analysis have opened new possibilities for building highly sensitive and intelligent diagnostic systems. In this study, an artificial intelligence-enhanced optical nanosensing framework is proposed for early and precision lung cancer detection through chromatin alteration analysis using advanced deep learning architectures and intelligent image processing techniques. The proposed framework is centered on the detection of nanoscale chromatin abnormalities that occur during the early stages of carcinogenesis. Optical nanosensing is employed to capture subtle variations in chromatin organization, nuclear texture, and intracellular structural patterns that are often beyond the detection capacity of conventional diagnostic methods. These optical and image-based signals are then processed through a multi-stage computational pipeline involving image acquisition, enhancement, denoising, segmentation, feature extraction, and classification. Advanced deep learning models, particularly convolutional neural networks and hybrid learning architectures, are integrated to automatically identify discriminative features associated with normal and abnormal chromatin configurations from microscopy or nanosensor-derived image datasets. To improve diagnostic reliability, the framework also incorporates intelligent image processing methods that enhance signal quality, reduce artifacts, and strengthen feature representation. This integration of optical nanosensing and deep learning enables more accurate classification while minimizing false-positive and false-negative outcomes. The proposed system is intended to provide a non-invasive, scalable, and clinically relevant solution for early-stage lung cancer screening, diagnostic support, and precision oncology applications. Moreover, by enabling fine-grained chromatin analysis, the framework may assist clinicians in disease stratification, progression assessment, and individualized treatment decision-making. This research contributes to the growing domain of AI-enabled biomedical diagnostics by presenting an interdisciplinary framework that combines nanoscale optical sensing, pathology-informed image analysis, and advanced artificial intelligence techniques. The proposed approach has strong potential to enhance diagnostic sensitivity, specificity, speed, and interpretability compared with conventional workflows. Overall, the study highlights a promising direction for next-generation lung cancer diagnostics, where intelligent optical nanosensing systems can support earlier detection, more precise diagnosis, and improved patient-centered clinical care.