Hardware Design of Lightweight Binary Classification Algorithms for Small-Size Images on FPGA

This study explores the implementation of lightweight binary classification algorithms on low-cost Field-Programmable Gate Arrays (FPGAs) for medical image analysis. Recognizing the growing demand for efficient and accurate diagnostic tools in healthcare, we focus on applying FPGAs to process small-sized medical images, explicitly targeting the detection of malaria from blood cell images. Our approach involves the hardware designs of k-nearest Neighbors (k-NN), Convolutional Neural Networks (CNN), and Decision Tree classifiers rigorously tested on a publicly available Malaria dataset. The methodology emphasizes the integration of these classifiers on FPGA, detailing the optimization strategies that allow for enhanced processing speed and reduced resource utilization. Comparative analysis reveals that our FPGA-based implementation significantly outperforms MATLAB simulations, achieving processing speeds more than thousands of times faster. Our proposed hardware design also requires fewer Look-up Tables (LUTs) than other classification studies in the literature, showcasing decreases of 73.9% for k-NN, 57% for CNN, and 96.7% for the Decision Tree classifier. Furthermore, results highlight the Decision Tree classifier as the most effective, with an accuracy rate of 99.33%, followed by CNN at 97.67% and k-NN at 95.33%. These findings demonstrate the capability of FPGAs in medical image classification and underscore their potential to revolutionize disease diagnosis processes, particularly in resource-limited environments.