Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
Visual cryptography (VC) is one of the best techniques used to secure information. It uses the human vision to decrypt the encrypted images without any cryptographic computations. The basic concept of visual cryptogra...Visual cryptography (VC) is one of the best techniques used to secure information. It uses the human vision to decrypt the encrypted images without any cryptographic computations. The basic concept of visual cryptography is splitting the secret image into shares such that when the shares are stacked, the secret image is revealed. In this paper we proposed a method that is based on the concept of visual cryptography for color images and without any pixel expansion which requires less space. The proposed method is used to encrypt halftone color images by generating two shares, random and key shares which are the same size as the secret color image. The two shares are generated based on a private key. At the receiving side, the secret color image is revealed by stacking the two shares and exploiting the human vision system. In this paper, we produce an enhanced form of the proposed method by modifying the encryption technique used to generate the random and the key shares. Experimental results have shown that the proposed and the enhanced methods suggest an efficient way to encrypt a secret color image with better level of security, less storage space, less time of computation and with a better value of PSNR.展开更多
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
文摘Visual cryptography (VC) is one of the best techniques used to secure information. It uses the human vision to decrypt the encrypted images without any cryptographic computations. The basic concept of visual cryptography is splitting the secret image into shares such that when the shares are stacked, the secret image is revealed. In this paper we proposed a method that is based on the concept of visual cryptography for color images and without any pixel expansion which requires less space. The proposed method is used to encrypt halftone color images by generating two shares, random and key shares which are the same size as the secret color image. The two shares are generated based on a private key. At the receiving side, the secret color image is revealed by stacking the two shares and exploiting the human vision system. In this paper, we produce an enhanced form of the proposed method by modifying the encryption technique used to generate the random and the key shares. Experimental results have shown that the proposed and the enhanced methods suggest an efficient way to encrypt a secret color image with better level of security, less storage space, less time of computation and with a better value of PSNR.
