GDL

Deteksi Kanker Serviks Otomatis Berbasis Jaringan Saraf Tiruan LVQ dan DCT

Journal from gdlhub / 2016-11-21 03:25:44
Oleh : Dhimas Arief Dharmawan, JNTETI
Dibuat : 2014-11-01, dengan 1 file

Keyword : Citra Pap Smear, Learning Vector Quantization (LVQ), Kanker Serviks, Discrete Cosine Transform (DCT)
Url : http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/114

Kanker serviks telah menjadi penyakit yang banyak diderita kaum wanita di dunia. Secara umum, kanker serviks baru terdeteksi setelah memasuki stadium lanjut, sebab kanker ini sulit teramati pada stadium awal. Pada penelitian ini dirancang perangkat lunak dengan jaringan saraf tiruan Learning Vector Quantizatin (LVQ), sebagai alat bantu deteksi kanker serviks. Sebelum dideteksi, dilakukan pengolahan citra terhadap citra sel serviks, yaitu preprocessing, peregangan kontras, median filter, operasi morfologi, segmentasi, dan ekstraksi fitur dengan Discrete Cosine Transform (DCT). Citra sel serviks yang digunakan berjumlah 73 buah yang terdiri atas lima puluh buah citra sel normal dan 23 buah citra sel kanker. Proses pelatihan LVQ menggunakan 35 buah citra sel normal dan empat belas buah citra sel kanker. Proses pengujian LVQ menggunakan 15 buah citra sel normal dan sembilan buah citra sel kanker. Dari hasil pengujian, didapatkan nilai sensitivitas, spesifisitas, dan akurasi sebesar 88,89 %, 100 %, dan 95,83 %.

Deskripsi Alternatif :

Cervical cancer has became the common women disease in the world. Mostly, cervical cancer has been already known lately, because it is very dificult to detect this in early stage. In this work, a computer based software using Learning Vector Quantization (LVQ) has been designed as the early cervical cancer detection aid tool. There are six methods before the detection is performed, namely preprocessing, contrast stretching, median filtering, morphology operation, image segmentation, and Discrete Cosine Transform based feature extraction. In tihis work, 73 cervical cell images that consist of 50 normal images and 23 cancer images are used. 35 normal images and 14 cancer images are used to train the LVQ. Then, 23 normal images and 9 cancer images are used in the testing process. Our results show 88,89 % cancer image can be detected correctly (sensitivity), 100 % normal image can be detected corerctly (specificity), and 95,83 % for overall detection (accuracy).

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	JNTETI
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com