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.

Detection of COVID-19 has been a very active field of research with thousands of papers published after outbreaks of COVID-19 in the world. Computer-Aided Design (CAD) based studies have a significant role in the medical field thanks to rapidly developing technology. To help radiologists speed up the diagnostic process, CAD with convolutional neural networks (CNN) can be used as decision support mechanisms. Furthermore, CNN has the power to learn various image features automatically, and it may offer an effective way for COVID-19 detection. In this paper, we propose a CNN design for COVID-19 detection. We used a data set of X-ray images collected from two publicly available sources. This data set consists of 400 images of which 200 are COVID-19 and 200 are healthy. First, we preprocessed all data sets and then divided them by randomly allocating 70 % for training and 30 % for the test. We obtained the accuracy, specificity, and sensitivity rate of our model as 96.11%, 98.89 %, and 93.33 %, respectively.

In this study, the dielectric measurement of homogeneous materials via short-circuited circular waveguides is examined. The proposed method is numerically tested against homogeneous materials with varying permittivities, as well as in different shapes and positions. Synthetic data is generated via the moment method solution of the related integral equation. In the inversion algorithm, Newton-Raphson method with multi-frequency data is used. The complex part of the material is modeled in a linear fashion.

Image processing is the most preferred technique in Computer-Aided Design (CAD) studies, and therefore the enhancement of image processing plays an essential role in the advancement of technology. The primary purpose of this study is to examine the effect of fixed threshold value on images of different sizes when using the binarization method in image processing. The analyzes are made based on the change in the detection accuracy percentage of the K-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) classification methods on the MATLAB software platform. At the same time, the effect of the binarization threshold value on images with different pixel dimensions (8x8, 16x16, 32x32, 64x64, and 128x128) are investigated. CNN classification obtained the best accuracy percentage in the used malaria disease blood cell data 97.5%, followed by k-NN with 95% and SVM with 91.5%.

In this study, Field Programmable Gate Array (FPGA) implementation of Convolutional Neural Network (CNN) for classification of malaria diseased cell is done. The hardware is designed and implemented on Xilinx Zynq-7000 FPGA using Very High-Speed Integrated Circuit Hardware Description Language (VHDL). In accordance with this purpose, Convolutional Neural Network (CNN) classification method used by image processing to make it easier for experts to comment on diseased cells. The classification method allows us to make a simpler interpretation by classifying complex images. Thanks to this research, it facilitates early diagnosis using image processing in the medical field as soon as reduces death and treatment costs. According to the experimental results, the accuracy rate for finding malaria diseased cell using CNN method is 94.76% for 200 8x8 binary images. The average execution time of CNN algorithm using Matlab on desktop PC is 174 microseconds. On the other hand, the maximum achievable frequency on Zynq FPGA is 168MHz (i.e. the longest critical path is 5.93 nanoseconds). The occupied area of CNN on Xilinx Zynq FPGA is only 783 six-input Look Up Tables (LUTs) of 17600, which is about 4.34% of Xilinx Zynq-7000 (XC7Z010-1CLG400C) FPGA.