Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques

Key Points

• Reliable classification of lung cancer was achieved through lightweight convolutional neural network architectures.
• The study found that these CNN architectures reached an accuracy of over 90% in histopathological imaging.
• Analysis involved histopathological imaging techniques applied to diverse lung cancer samples.
• The findings suggest that lightweight CNNs may enhance clinical decision-making in lung cancer diagnostics.