Early Detection of Lung Cancer Using Computer Aided Tomography Images

Mhatre, Varun; Vispute, Vishwesh; Talele, Kiran; Mishra, Nitin

doi:10.1109/icccnt51525.2021.9579977

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-resolution images have more details, and these details are of great significance in practical applications such as diagnosis of diseases [ 19 ]. Image super-resolution technology has always been a research hotspot in aerospace, remote sensing, target recognition, and other fields [ 20 ]. Image super-resolution (SR) technology has been widely applied, with high practical value in medical imaging, face recognition, high-definition audio, video, and other fields.…”

Section: Introductionmentioning

confidence: 99%

“…From a statistical point of view, this process can be considered to establish a conditional probability pðyjxÞ, where x is the input low-resolution image and y is the corresponding high-resolution image. Through training, the neural network can learn to obtain the statistical characteristics of lowresolution images and restore high-resolution images accordingly, that is, generalize from the training data set to the test data set [18][19][20]. Image super-resolution reconstruction based on deep neural networks can be roughly divided into two research directions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Qin

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%