Deep Learned Singular Residual Network for Super Resolution Reconstruction

Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based on deep learning to achieve super resolution(SR)by utilizing deep singular-residual neural network(DSRNN)in training phase.Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs.Singular value decomposition(SVD)is applied to each LR-residual image pair to decompose into subbands of low and high frequency components.Later,DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network.With fewer layers in DSRNN,the influence of exploding gradients is reduced.This speeds up the learning process and also improves accuracy by using skip connections.The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase.Experimental analysis shows that the proposed method results in superior performance to existingmethods in terms of subjective quality.Extensive testing results on popular benchmark datasets such as set5,set14,and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.

Intelligent classification of lung malignancies using deep learning techniques

Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of the early investigations used CT,magnetic resonance and mammography imaging.Using appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer.All of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the patients.To address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were utilized.Design/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early stage.In certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly detected.Deep learning is a new,capable and influential approach for predicting medical images.In this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung nodules.Convolutional neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image dataset.Findings-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan images.According to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google Collaborator.Originality/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and DenseNet169.The results are validated by computing the confusion matrix for each network type.