Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen
There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosis image through rontgen picture or X-ray. Then, it is analyzed manually by Rheumatology experts. Article present the creation of a system which could detect osteoporosis disease on human, by...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Bina Nusantara University
2008-05-01
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| Series: | CommIT Journal |
| Online Access: | https://journal.binus.ac.id/index.php/commit/article/view/494 |
| _version_ | 1827846746043056128 |
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| author | Is Mardianto Dian Pratiwi |
| author_facet | Is Mardianto Dian Pratiwi |
| author_sort | Is Mardianto |
| collection | DOAJ |
| description | There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosis
image through rontgen picture or X-ray. Then, it is analyzed manually by Rheumatology experts. Article present the creation
of a system which could detect osteoporosis disease on human, by implementing the Rheumatology principles. The main areas
identified were between wrist and hand fingers. The working system in this software included 3 important processing, which
were process of basic image processing, pixel reduction process, pixel reduction, and artificial neural networks. Initially, the
color of digital X-ray image (30 x 30 pixels) was converted from RGB to grayscale. Then, it was threshold and its gray level
value was taken. These values then were normalized to an interval [0.1, 0.9], then reduced using a PCA (Principal Component
Analysis) method. The results were used as input on the process of Backpropagation artificial neural networks to detect the
disease analysis of X-ray being inputted. It can be concluded that from the testing result, with a learning rate of 0.7 and
momentum of 0.4, this system had a success rate of 73 to 100 percent for the non-learning data testing, and 100 percent for
learning data.
Keywords: osteoporosis, image processing, PCA, artificial neural networks |
| first_indexed | 2024-03-12T09:21:39Z |
| format | Article |
| id | doaj.art-da642c91f4f8480e9427ce27c9ed69a9 |
| institution | Directory Open Access Journal |
| issn | 1979-2484 2460-7010 |
| language | English |
| last_indexed | 2024-03-12T09:21:39Z |
| publishDate | 2008-05-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | CommIT Journal |
| spelling | doaj.art-da642c91f4f8480e9427ce27c9ed69a92023-09-02T14:31:26ZengBina Nusantara UniversityCommIT Journal1979-24842460-70102008-05-0121698010.21512/commit.v2i1.494482Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang EigenIs Mardianto0Dian Pratiwi1Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Trisakti, Jln. Kyai Tapa No.1, Grogol, Jakarta Barat 11440Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Trisakti, Jln. Kyai Tapa No.1, Grogol, Jakarta Barat 11440There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosis image through rontgen picture or X-ray. Then, it is analyzed manually by Rheumatology experts. Article present the creation of a system which could detect osteoporosis disease on human, by implementing the Rheumatology principles. The main areas identified were between wrist and hand fingers. The working system in this software included 3 important processing, which were process of basic image processing, pixel reduction process, pixel reduction, and artificial neural networks. Initially, the color of digital X-ray image (30 x 30 pixels) was converted from RGB to grayscale. Then, it was threshold and its gray level value was taken. These values then were normalized to an interval [0.1, 0.9], then reduced using a PCA (Principal Component Analysis) method. The results were used as input on the process of Backpropagation artificial neural networks to detect the disease analysis of X-ray being inputted. It can be concluded that from the testing result, with a learning rate of 0.7 and momentum of 0.4, this system had a success rate of 73 to 100 percent for the non-learning data testing, and 100 percent for learning data. Keywords: osteoporosis, image processing, PCA, artificial neural networkshttps://journal.binus.ac.id/index.php/commit/article/view/494 |
| spellingShingle | Is Mardianto Dian Pratiwi Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen CommIT Journal |
| title | Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen |
| title_full | Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen |
| title_fullStr | Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen |
| title_full_unstemmed | Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen |
| title_short | Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen |
| title_sort | sistem deteksi penyakit pengeroposan tulang dengan metode jaringan syaraf tiruan backpropagation dan representasi ciri dalam ruang eigen |
| url | https://journal.binus.ac.id/index.php/commit/article/view/494 |
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