Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models

Abstract

Abstract 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.