An End-to-End Deep Learning Pipeline for Enhanced Liver Ultrasound Imaging and Hepatitis Classification

Ultrasound imaging is an important non-invasive technique to evaluate chronic infections like hepatitis, but the diagnostic accuracy of the method is low due to speckle noise, poor resolution, and the lack of labeled clinical data. Conventional deep learning models have been found to perform well, but are usually limited by the lack of adequate training samples and inefficient processing of noise and texture in ultrasound. To address these problems, this paper suggests a new three-step pipeline combining synthetic data generation, denoising, and super-resolution with subsequent validation using a baseline classification model. An Adaptive Conditional GAN with diffusion modeling is used to generate a synthetic dataset for the hepatitis affected liver ultrasound images with pathological variability. A Multi-Scale Attention CycleGAN denoiser is used to remove speckle noise while preserving both texture and fidelity. Along with that, the Residual Dense Transformer Super-Resolution module was used to boost structural and fine-grained information of the ultrasound images by fusing the cross-scale features. Then, several deep learning classifiers such as ResNet, DenseNet, EfficientNet, and Hybrid_CNN (ResNet-DenseNet) were employed to evaluate the diagnostic utility of the reconstructed images. Extensive experiments revealed that the proposed framework (Epoch 500) performed better than state-of-the-art approaches in PSNR (28 dB), SSIM (0.75), MSE (0.010), MAE (0.020), and LPIPS (0.150) of image reconstruction. The EfficientNet classifier achieved an overall average classification accuracy of 90% at Epoch 500. These findings suggest that the proposed model produces high-fidelity ultrasound images and empowers trustworthy staging of hepatitis.