Sistem Deteksi Penyakit Pengeroposan Tulang Dengan Metode Jaringan Syaraf Tiruan Backpropagation Dan Representasi Ciri Dalam Ruang Eigen
DOI:
https://doi.org/10.21512/commit.v2i1.494Abstract
There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosisimage 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
Plum Analytics
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)