Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification

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  3. Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification
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Issue Vol. 5 No. 01 (2021)
Published 8 January 2022
Section Articles
Pages 34-42
doi.org/10.25124/ijait.v5i01.3220 file_copy
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Abstract

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.

Keywords: Feed-Forward, Artificial, Neural-Network, FPGA, Xilinx, Spartan 3A, Blood type classification

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[1]
Priramadhi, R.A. and Darlis, D. 2022. Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification. IJAIT (International Journal of Applied Information Technology). 5, 01 (Jan. 2022), 34–42. DOI:https://doi.org/10.25124/ijait.v5i01.3220.
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