Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

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Abstract

Lung cancer remains one of the most common and deadly diseases globally, necessitating swift and precise detection to enhance patient prognosis. Conventional radiological techniques, including CT scans and X-rays, often exhibit high false positive rates, low sensitivity, and reliance on radiologist interpretation, leading to potential diagnostic inconsistencies. This research presents a generative AI-driven convolutional neural network (CNN) framework aimed at improving lung cancer detection, risk prediction, and treatment planning. The proposed approach integrates deep learning, CNN-based feature extraction, and generative adversarial networks (GANs) for data augmentation, effectively overcoming limitations in AI-powered diagnostics. A dataset comprising lung cancer-patient information, CT scan characteristics, and cancer risk scores was utilized for model training and evaluation. The abstract has been updated to clearly highlight the quantitative improvements, showing an increase in accuracy from 94.5% to 100% and an improvement in AUC from 0.95 to 1.00 compared with existing models. The CNN model extracts crucial features from medical images, whereas GAN-generated synthetic data enhances learning efficiency and robustness. The model was implemented using TensorFlow and Keras, optimized with Adam and trained over 30 epochs, achieving an unparalleled 100% accuracy, precision, recall, and F1-score on the validation dataset. A comparative analysis with state-of-the-art AI methodologies, including Transformers and hybrid deep learning architectures, demonstrated the superior efficacy of the proposed framework. The findings highlight that generative AI significantly refines lung cancer diagnostics by minimizing false positives and optimizing treatment recommendations. The novelty of this work lies in its unified framework that brings together synthetic data generation, advanced feature extraction, attention-based classification, and a probabilistic risk prediction approach into a single system, an integration that has not been comprehensively explored in existing studies. The ROC-AUC score of 1.00 further validates the model's ability to accurately differentiate between malignant and benign cases. Future advancements will focus on clinical deployment, explainable AI (XAI) for improved interpretability, and integration into real-world healthcare systems.

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