IDENTIFIKASI CITRA RADIOGRAFI PANORAMIK UNTUK MEMBEDAKAN PENYAKIT KISTA DAN TUMOR PADA RONGGA MULUT DENGAN METODE BACKPROPAGATION
| Main Author: | Millasari, Chindy Puspita |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2016
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| Subjects: | |
| Online Access: |
https://eprints.untirta.ac.id/12688/1/IDENTIFIKASI%20CITRA%20RADIOGRAFI%20PANORAMIK%20UNTUK%20MEMBEDAKAN%20PENYAKIT%20KISTA%20DAN%20TUMOR%20PADA%20RONGGA%20MUL.PDF https://eprints.untirta.ac.id/12688/ |
Daftar Isi:
- The oral cavity is the main door entry of food into our bodies. But too often the oral hygiene becomes less attention, although not rare oral cavity diseases originated from the leftovers stacked and not cleaned. One of the diseases of the oral cavity are often found in Poli Dental Hospitals in Indonesia are cysts and tumors. Determination of cysts and tumors through paranomik image can hardly be distinguished by naked eye, depending on the knowledge and experience of the dentist as well as on the results of the analysis of the radiologist. The preferred solution is to help the process of identifying lesions cysts and tumors in vast amounts of data, but with a relatively shorter time with the aid of a computer (automatic). The purpose of this study was to determine how the identification to distinguish cysts and tumors in the oral cavity which has some similarities clinical characteristics with a series of segmentation with active contour, feature extraction with GLCM (gray level cooccurence matrix) and classification with artificial neural network, backpropagation using MATLAB software. Backpropagation network will be trained three category of data cysts, tumors and not both will then be tested 10 times by doing pole method is exchanging training data and test data. The percentage of success is based on 10 test using artificial neural network, backpropagation amounted to 95.42%. While the percentage of success is based on identification using artificial neural network, backpropagation of 93.89% with a percentage of 95.3% of cysts, tumors of 95.3% and not both the size of 91.08%. This system can identify lesions cysts and tumors in the mouth of vast amounts of data and with a relatively short time.