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Accurate lung cancer detection is vital for timely diagnosis and treatment. This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet-50, VGG-16, ResNet-101, VGG-19, DenseNet-201, and EfficientNet-B4, using the LIDC-IDRI dataset. Models were assessed both in their base forms and with transfer learning. The dataset consisted of 460 × 460 × 3 pixel images categorized into squamous cell carcinoma (SCC), normal benign, large cell carcinoma (LCC), and adenocarcinoma (ADC). Performance metrics were computed, including accuracy (99.47% for the custom CNN), precision (99.50%), recall (98.37%), AUC (99.98%), and F1-score (98.98%) during training. However, overfitting was observed in the validation phases. Transfer learning models showed better generalization, with DenseNet-201 achieving a top validation accuracy of 96.88% and EfficientNet-B4 of 96.53%. Hyperparameter tuning improved the models’ generalization capabilities, maintaining high accuracy while reducing overfitting. This study highlights the effectiveness of transfer learning, particularly DenseNet-201, in enhancing automated lung cancer detection systems. Future work will focus on expanding datasets and exploring additional augmentation techniques to further refine model performance in clinical settings.