Hardware Co-Simulation Based Deep Neural Networks for Liver Diseases Detection in MRI Images

Accurate characterization of focal liver lesions on multiphasic magnetic resonance imaging (MRI) is central to early detection and treatment planning; however, clinical deployment of deep learning remains constrained by compute cost, latency, and the need for rigorous verification of hardware implementations. This paper presents LiverNet-Q, a hardware-aware, multi-phase deep neural network for multi-class liver disease detection from MRI, coupled with an end-to-end hardware co-simulation workflow that validates functional equivalence between the software model and the synthesized register-transfer level (RTL) design. The proposed pipeline first localizes the liver with a lightweight U-Net trained using public liver MRI annotations, then classifies lesions into five clinically relevant categories (normal liver, hepatocellular carcinoma, hemangioma, focal nodular hyperplasia, and simple cyst) using attention-based phase fusion. To enable resource-efficient inference, LiverNet-Q is trained with quantization-aware training and deployed in INT8 precision. The accelerator is implemented with Vitis HLS using a streaming dataflow micro-architecture that targets an initiation interval of one for core convolution operators. Experiments on public benchmarks demonstrate that INT8 deployment preserves diagnostic performance with a small loss relative to FP32 while providing substantial speedups. Hardware co-simulation reports confirm cycle-accurate latency and throughput, supporting reproducible, deployment-ready evaluation.