ROI Based Pixel Segmentation for Human Blood Type Classification by Neural Network

The identification of human blood type stillrequires a fast and accurate device considering the number ofblood samples that need to be distributed and transfusedimmediately. In this study we propose a hardwareimplementation of human blood type identification devices usingfeedforward neural network algorithms on grayscale images ofblood samples. The images to be used are 32x32 pixels, 48x48pixels, 64x64, 80x80, and 9x96 pixels. The algorithm wereimplemented using VHSIC Hardware Description Language.With artifical neural network implemented on Xilinx FPGASpartan 3S1000, the success rate of detection by grouping by themean and median ratios of the number of '1' bits is more than75%.

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Peramalan Kebutuhan Darah Jenis Packet Red Cells (PRC) di PMI Kota Surabaya dengan Metode Jaringan Syaraf Tiruan Propagasi Balik

Zeta - Math Journal ◽

10.31102/zeta.2018.4.1.7-11 ◽

2018 ◽

Vol 4 (1) ◽

pp. 7-11

Author(s):

Azmi Khulmala Devi ◽

Teguh - Herlambang

Keyword(s):

Neural Network ◽

Human Body ◽

Human Blood ◽

Blood Cells ◽

Red Cells ◽

Blood Type ◽

Whole Body ◽

Red Cross

Human blood is liquid in human body, which functions to transport oxigen needed by cells to the whole body. Considering the important blood function, the Indonesian Red Cross (PMI) has to maintain its blood stock stability to ensure the blood availibility. But the problem that PMI has to encounter with is its blood over-supply which leads to blood disposal. To minimize its unnessary blood disposal, estimation of blood need is required. Data of blood demand is normalized first, then estimation is made using Neural Network Backpropagation. In this study the estimation is made to the blood type of Packet Red Cells (PRC), the blood cells stocked at PMI Kota Surabaya. The best simulation result is at epoch 3000 with function Y = 4542,33 – 1,64595 x – 0,244018 x^2 and an error of 0,020314.

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

IJAIT (International Journal of Applied Information Technology) ◽

10.25124/ijait.v5i01.3220 ◽

2022 ◽

Vol 5 (01) ◽

pp. 34

Author(s):

Rizki Ardianto Priramadhi ◽

Denny Darlis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Blood Type ◽

Feed Forward ◽

System Input ◽

Neural Network Algorithm ◽

Hardware Description ◽

Artificial Neural ◽

Hidden Layer ◽

Type Classification

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.

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Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

10.31227/osf.io/dp63x ◽

2018 ◽

Author(s):

Rizki Eka Putri ◽

Denny Darlis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Low Cost ◽

Image Resolution ◽

Blood Type ◽

Feed Forward ◽

Testing Facility ◽

System Input ◽

Artificial Neural ◽

Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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Exploration on human blood type case in teaching practice of genetics

Hereditas (Beijing) ◽

10.3724/sp.j.1005.2013.01040 ◽

2013 ◽

Vol 35 (8) ◽

pp. 1040-1044

Author(s):

Yan PI ◽

Xiao-Ying LI ◽

Cong HUAI ◽

Shi-Ming WANG ◽

Shou-Yi QIAO ◽

...

Keyword(s):

Human Blood ◽

Teaching Practice ◽

Blood Type

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Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

Applied Sciences ◽

10.3390/app11146613 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6613

Author(s):

Young-Bin Jo ◽

Jihyun Lee ◽

Cheol-Jung Yoo

Keyword(s):

Neural Network ◽

Average Rate ◽

Convolution Neural Network ◽

Clone Detection ◽

Code Clones ◽

Classification Technique ◽

Development Costs ◽

Code Quality ◽

Type Information ◽

Type Classification

Appropriate reliance on code clones significantly reduces development costs and hastens the development process. Reckless cloning, in contrast, reduces code quality and ultimately adds costs and time. To avoid this scenario, many researchers have proposed methods for clone detection and refactoring. The developed techniques, however, are only reliably capable of detecting clones that are either entirely identical or that only use modified identifiers, and do not provide clone-type information. This paper proposes a two-pass clone classification technique that uses a tree-based convolution neural network (TBCNN) to detect multiple clone types, including clones that are not wholly identical or to which only small changes have been made, and automatically classify them by type. Our method was validated with BigCloneBench, a well-known and wildly used dataset of cloned code. Our experimental results validate that our technique detected clones with an average rate of 96% recall and precision, and classified clones with an average rate of 78% recall and precision.

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