High-Efficiency FPGA Implementation of Deep Learning Models for Colorectal Tumor Image Analysis

To meet the demand for real-time, energy-efficient solutions in medical diagnostics, this research offers a high-efficiency FPGA implementation of a convolutional neural network (CNN) for colorectal tumor picture processing. Loop unrolling, pipelining, and effective memory use were among the methods used in the creation of the CNN model, which had an optimized system architecture. With a lowered latency of 10.2 milliseconds and power usage of 4.8 watts, the model achieved an accuracy of 98.5% during training and testing on the TCGA Colorectal Tumor Dataset. With a throughput of 120 frames per second, the implementation from 2021 to 2023 significantly outperformed earlier ones. These findings highlight the possibility of FPGA-based deep learning models to provide real-time, high-performance analysis at a lower energy cost. According to the results, this strategy not only improves diagnostic precision but also provides a practical, scalable solution for clinical setting deployment, opening the door for a wider use of FPGA-based systems in medical image processing.