Dual attention mechanism network for lung cancer images super-resolution

Background and objective: Currently, the morbidity and mortality of lung cancer rank first among malig-nant tumors worldwide. Improving the resolution of thin-slice CT of the lung is particularly important for the early diagnosis of lung cancer screening. Methods: Aiming at the problems of network training difficulty and low utilization of feature informa-tion 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 fea-ture 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. Results: The results on the Set5 dataset for 4 x 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. Conclusion: Through the DAMN reconstruction method, the image quality can be improved without in-creasing 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. (c) 2022 Elsevier B.V. All rights reserved.