Lung Cancer Classification Using Deep Learning Models for Edge Computing. A Comparative Analysis

Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Early diagnosis and treatment before the cancer spreads can significantly improve survival rates. Edge devices, including smartphones, wearables, IoT sensors, and embedded systems, are increasingly important for deploying deep learning models in lung cancer detection. In this study, we evaluated three state-of-the-art, compact deep learning models, NasNetMobile, MobileNetV2, and EfficientNetV2, for lung cancer classification using CT scan medical images. Two public datasets, the IQ-OTH/NCCD lung cancer dataset and the Chest CT-Scan images dataset, were employed to assess model performance. EfficientNetV2 emerged as the top performer, achieving accuracies of 98.64% and 89.52% across the datasets, respectively. Its compact size and computational efficiency underscore its potential for deployment in resource-constrained edge computing environments, enabling real-time, scalable diagnostic solutions.