Lung Cancer Classification Using Effective Fusion Network Integrating Transformers and Controllable Convolutional...

In this work, a new fusion network was developed and applied to lung cancer classification. It incorporates a transformer-based module, a convolutional module with encoders, and another convolutional module with decoders. Each module is strategically placed and extracts features at different scales, enabling the network to capture enriched feature information at both global and local levels. A novel hybrid loss function was also employed to reduce both pixel- and image-based differences while enhancing region-wise consistency. The model's effectiveness was evaluated by classifying lung cancer subtypes from computed tomography scans, a highly challenging task due to factors such as high interclass similarity and the presence of nontumor features. Moreover, recent methods used for lung cancer classification were applied to identical datasets and evaluated using identical metrics to ensure fair comparative assessments. The results demonstrate the superiority of the proposed approach in lung cancer subtype classification, achieving higher accuracy (96.59%), recall (96.68%), precision (96.90%), and F1-score (96.65%) compared to recent methods.