Multimodal AI Framework for Lung Cancer Diagnosis: Integrating CNN and ANN Models for Imaging and Clinical Data Analysis

Lung cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for early and accurate diagnostic tools. This study presents a novel multimodal artificial intelligence (AI) framework integrating convolutional neural networks (CNNs) and artificial neural networks (ANNs) to enhance lung cancer detection and classification. The CNN model was trained on a dataset of 900 CT images, meticulously preprocessed and categorized into four classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal tissue. Using advanced techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping), the CNN provided interpretable predictions with a weighted average accuracy of 91%, achieving near-perfect AUC values for most classes. Simultaneously, an ANN was developed using clinical data from 999 patients, incorporating 24 features that captured demographic, symptomatic, lifestyle, and genetic factors. The ANN exhibited an exceptional classification performance, attaining an overall accuracy of 99% with minimal misclassifications. Together, these models form a robust diagnostic system that leverages the complementary strengths of imaging and clinical data analysis to improve diagnostic accuracy and clinical decision-making. The proposed framework not only delivers precise classification but also emphasizes interpretability, scalability, and clinical applicability. While the CNN excels in imaging-based diagnosis, the ANN offers reliable predictions grounded in patient-specific clinical data. Future work will focus on expanding datasets, incorporating additional diagnostic modalities, and optimizing the models for real-world clinical deployment. This study highlights the transformative potential of multimodal AI in advancing the early detection and management of lung cancer.