Clinical-ready CNN framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency

Purpose

This study develops a computationally efficient Convolutional Neural Network (CNN) for lung cancer classification in Computed Tomography (CT) images, addressing the critical need for accurate diagnostic tools deployable in resource-constrained clinical settings.

Methods

Using the IQ-OTH/NCCD dataset (1190 CT images: normal, benign, and malignant classes from 110 patients), we implemented systematic architecture optimization with strategic data augmentation to address class imbalance and limited dataset challenges. Patient-level data splitting prevented leakage, ensuring valid performance metrics. The model was evaluated using 5-fold cross-validation and compared against established architectures using McNemar's test for statistical significance.

Results

The optimized CNN achieved 94 % classification accuracy with only 4.2 million parameters and 18 ms inference time. Performance significantly exceeded AlexNet (85 %), VGG-16 (88 %), ResNet-50 (90 %), InceptionV3 (87 %), and DenseNet (86 %) with p < 0.05. Malignant case detection showed excellent clinical metrics (precision: 0.96, recall: 0.95, F1-score: 0.95), critical for minimizing false negatives. Ablation studies revealed data augmentation contributed 6.6 % accuracy improvement, with rotation and translation proving most effective. The model operates 4.3 × faster than ResNet-50 while using 6 × fewer parameters, enabling deployment on standard clinical workstations with 4–8 GB GPU memory.

Conclusions

Carefully optimized CNN architectures can achieve superior diagnostic performance while meeting computational constraints of real-world medical settings. Our approach demonstrates that systematic optimization strategies effectively balance accuracy with clinical deployment feasibility, providing a practical framework for implementing AI-assisted lung cancer detection in resource-limited healthcare environments. The model's high sensitivity for malignant cases positions it as a valuable clinical decision support tool.