Guangzhou First People's Hospital

Classifying small cell lung cancer histopathology requires resolving high-frequency nuclear texture and long-range tissue organisation at once, which fixed-kernel convolutions and pure transformers each handle poorly. SCLC-SBT puts frequency-domain channel recalibration in its first three stages and a dual convolutional and 2D selective state-space branch in its deepest stage, reaching 0.9697 accuracy, 0.9728 F1 and 0.9971 AUC on a 1,974-patch binary benchmark. Against the six strongest baselines the difference is not statistically significant on the 297-sample test set, so its clear advantage is competitive accuracy at about 17M parameters and 7.0G FLOPs.

Classifying small cell lung cancer histopathology requires resolving high-frequency nuclear texture and long-range tissue organisation at once, which fixed-kernel convolutions and pure transformers each handle poorly. SCLC-SBT puts frequency-domain channel recalibration in its first three stages and a dual convolutional and 2D selective state-space branch in its deepest stage, reaching 0.9697 accuracy, 0.9728 F1 and 0.9971 AUC on a 1,974-patch binary benchmark. Against the six strongest baselines the difference is not statistically significant on the 297-sample test set, so its clear advantage is competitive accuracy at about 17M parameters and 7.0G FLOPs.