Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models
Résumé
By eliminating the need to alter the source images, this paper introduces a secure technique for coverless image steganography that strengthens defense against steganalysis attacks. Our method makes use of a hybrid Generative Adversarial Network (GAN) with a Support Vector Machine (SVM), which is trained and validated on a Diffusion Weighted Imaging (DWI) dataset to retain visually indistinguishable steganographic representations while increasing security. A powerful feature extraction capability of several Deep Learning Models (DLMs), EfficientNet-B4, DenseNet121, and Residual Network-18 (ResNet-18), integrated with the Vision Transformer (ViT) is performed. With the highest Peak Signal-to-Noise Ratio (PSNR) of 45.87 dB and Structural Similarity Index (SSIM) of 0.98, the ViT-GAN-SVM model exceeds other suggested models in terms of steganographic quality. Additionally, the ViT-GAN-SVM system achieves 99.78% accuracy, 99.85% sensitivity, 98.99% precision, and 99.85% F1-Score in terms of diagnostic accuracy. The ViT-GAN-SVM model performs much better than other introduced models in all diagnostic performance metrics, with increases ranging from 5.55% to 6.36%. This shows that ViT-GAN-SVM is a superior choice for medical diagnostic tasks since it can correctly identify prostate cancer on the DWI prostate cancer dataset.
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