LungNet-CT: A Unified Deep Learning Framework for Efficient Lung Cancer Classification from CT Images

Abstract

Early and accurate detection of Lung Cancer remains a major challenge in medical imaging. This paper investigates deep learning approaches for the classification of lung CT images into three categories: normal, benign, and malignant. Three convolutional neural network architectures are evaluated, namely a custom CNN, MobileNetV2, and Xception. The proposed workflow relies on a two-stage data strategy. The models are first trained on the LIDC-IDRI dataset to learn general lung and nodule-related features, and then fine-tuned on IQ-OTH/NCCD for the target three-class classification task. A unified preprocessing pipeline is applied, including resizing, rescaling, label encoding, batching, and shuffling. Performance is assessed using accuracy, loss, recall, and F1-score, with an additional 5-fold cross-validation experiment to evaluate robustness. The results show that Xception achieved the best predictive performance, with an accuracy of 0.993 and an F1-score of 0.992. MobileNetV2 also delivered strong results, reaching an accuracy of 0.982 and an F1-score of 0.981, while offering lower computational cost. The custom CNN remained competitive, with an accuracy of 0.961 and an F1-score of 0.930. The cross-validation results further supported the effectiveness of the workflow, highlighting a clear performance–efficiency trade-off between the evaluated models. The source code is publicly available on LungNet-CT.