Open AccessPrototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification
Author(s) -
Rizki Ardianto Priramadhi,
Denny Darlis
Publication year - 2022
Publication title -
ijait (international journal of applied information technology )
Language(s) - English
Resource type - Journals
ISSN - 2581-1223
DOI - 10.25124/ijait.v5i01.3220
Subject(s) - field programmable gate array , artificial neural network , backpropagation , computer science , spartan , rapid prototyping , computer hardware , artificial intelligence , layer (electronics) , feedforward neural network , pattern recognition (psychology) , engineering , materials science , nanotechnology , mechanical engineering
In this research, a Feed-Forward Artificial Neural Network design was implemented on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board and prototyped blood type classification device. This research uses blood sample images as a system input. The system was built using VHSIC Hardware Description Language to describe the feed-forward propagation with a backpropagation neural network algorithm. We use three layers for the feed-forward ANN design with two hidden layers. The hidden layer designed has two neurons. In this study, the accuracy of detection obtained for four-type blood image resolutions results from 86%-92%, respectively.