Lung Cancer Classification Using Effective Fusion Network Integrating Transformers and Controllable Convolutional Encoders–Decoders

Key Points

• Classifying lung cancer types using an integrated fusion network enhances diagnostic accuracy and efficiency.
• The model combines transformers and controllable convolutional encoders for improved performance in cancer classification.
• Methods involved building a sophisticated network architecture to effectively analyze and interpret lung cancer imaging data.
• These findings may enable advanced diagnostic tools, potentially transforming how lung cancer is identified and treated.