Lung Cancer Detection and Classification using CT Scan Image Processing

Nawreen, Nusraat; Hany, Umma; Islam, Tahmina

doi:10.1109/acmi53878.2021.9528297

Cited by 13 publications

(2 citation statements)

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“…Ignore this information. In the process of superresolution reconstruction, literature [11][12][13][14] all use Equation (6) as the image degradation model, directly discarding the subpixel information which will inevitably affect the accuracy of the reconstruction model. This paper tries to base on the degradation model ( 5) which establishes a super-resolution reconstruction model based on subpixel displacement to improve the accuracy of the reconstruction model.…”

Section: Image Degradationmentioning

confidence: 99%

“…The complete polarization information can be obtained by acquisition [ 12 ]. The system has gradually become a research hotspot due to its advantages of obtaining multiple Stokes vectors at the same time, simple structure, and easy implementation with respect to dynamic targets [ 13 ]. A direct mapping function is established between the sensor pixels and the scene to obtain enhanced images with the new computational imaging system [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network

Liu

Qin

et al. 2022

BioMed Research International

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The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

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