Dual attention mechanism network for lung cancer images super-resolution

Currently, the morbidity and mortality of lung cancer rank first among malignant tumors worldwide. Improving the resolution of thin-slice CT of the lung is particularly important for the early diagnosis of lung cancer screening.

Aiming at the problems of network training difficulty and low utilization of feature information caused by the deepening of network layers in super-resolution (SR) reconstruction technology, we propose the dual attention mechanism network for single image super-resolution (SISR). Firstly, the feature of a low-resolution image is extracted directly to retain the feature information. Secondly, several independent dual attention mechanism modules are constructed to extract high-frequency details. The introduction of residual connections can effectively solve the gradient disappearance caused by network deepening, and long and short skip connections can effectively enhance the data features. Furthermore, a hybrid loss function speeds up the network's convergence and improves image SR restoration ability. Finally, through the upsampling operation, the reconstructed high-resolution image is obtained.

The results on the Set5 dataset for 4 × enlargement show that compared with traditional SR methods such as Bicubic, VDSR, and DRRN, the average PSNR/SSIM is increased by 3.33 dB / 0.079, 0.41 dB / 0.007 and 0.22 dB / 0.006 respectively. The experimental data fully show that DAMN can better restore the image contour features, obtain higher PSNR, SSIM, and better visual effect.

Through the DAMN reconstruction method, the image quality can be improved without increasing radiation exposure and scanning time. Radiologists can enhance their confidence in diagnosing early lung cancer, provide a basis for clinical experts to choose treatment plans, formulate follow-up strategies, and benefit patients in the early stage.