Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study

Background: Deep learning (DL) shows promise for automated lung cancer diagnosis, but limited clinical data can restrict performance. While data augmentation (DA) helps, existing methods struggle with chest computed tomography (CT) scans across diverse DL architectures. Objective: This study proposes Random Pixel Swap (RPS), a novel DA technique, to enhance diagnostic performance in both convolutional neural networks and transformers for lung cancer diagnosis from CT scan images. Methods: RPS generates augmented data by randomly swapping pixels within patient CT scan images. We evaluated it on ResNet, MobileNet, Vision Transformer, and Swin Transformer models, using 2 public CT datasets (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases [IQ-OTH/NCCD] dataset and chest CT scan images dataset), and measured accuracy and area under the receiver operating characteristic curve (AUROC). Statistical significance was assessed via paired t tests. Results: The RPS outperformed state-of-the-art DA methods (Cutout, Random Erasing, MixUp, and CutMix), achieving 97.56% accuracy and 98.61% AUROC on the IQ-OTH/NCCD dataset and 97.78% accuracy and 99.46% AUROC on the chest CT scan images dataset. While traditional augmentation approaches (flipping and rotation) remained effective, RPS complemented them, surpassing the performance findings in prior studies and demonstrating the potential of artificial intelligence for early lung cancer detection. Conclusions: The RPS technique enhances convolutional neural network and transformer models, enabling more accurate automated lung cancer detection from CT scan images.

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