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Sistem pengklasifikasian kanker payudara berbasis ciri gray level co occurrence matrix glcm dan discrete wavelet transform dwt menggunakan support vector machine svm = Breast cancer classification system based on gray level co occurrence matrix glcm and discrete wavelet transform dwt features using support vector machine svm

Putri Utami; Dodi Sudiana, supervisor; Fransiskus Astha Ekadiyanto, examiner; Abdul Halim, examiner; Prima Dewi Purnamasari, examiner ([Publisher not identified] , 2015)

 Abstrak

[ABSTRAK
Kanker payudara adalah tumor ganas yang tumbuh akibat pertumbuhan sel-sel
jaringan yang tidak normal pada jaringan payudara. Kanker payudara pada wanita
merupakan penyakit yang kini paling banyak diderita dibandingkan jenis kanker
lainnya. Cara yang dilakukan agar penyakit ini tidak memiliki kesempatan untuk
menyebar adalah dengan mendeteksinya sedini mungkin dengan menggunakan
mammografi.
Pada penelitian ini penulis telah merancang suatu sistem yang menggunakan
komputer untuk mendeteksi dan mengklasifikasi kanker payudara pada citra
mammogram. Citra mammogram yang digunakan adalah citra mammogram dari
Mommographic Image Analysis Society (MIAS) yang terdiri dari 322 citra.
Pengolahan awal citra pada sistem ini menggunakan metode Otsu Thresholding,
pendeteksian tepi dengan menggunakan metode Canny, dan metode dilasi. Ciri
yang digunakan pada sistem ini adalah Gray Level Co-occurrence Matrix
(GLCM) dan Discrete Wavelet Transform (DWT). Metode pengklasifikasian yang
digunakan pada penelitian ini adalah Support Vector Machine (SVM).
Sistem memiliki ketahanan yang baik terhadap noise salt and pepper pada nilai
noise tertentu pada tiap jenis citra mammogram yang digunakan. Tingkat
keakuratan berkisar 80% pada saat diberi noise sebesar -16dB pada citra
mammogram jinak dan ganas. Keakuratan sistem juga teruji cukup baik untuk
jumlah data latih yang hanya sebesar 70% dimana tingkat keakuratan
pendeteksian dan pengklasifikasian adalah sebesar 80,6%.
ABSTRACT
Breast cancer is a malignant tumor that grows as a result of the growth of tissue
cells that are not normal in the breast tissue. Breast cancer in women is a disease
that is now the most common cancer than other types. How that is done so that the
disease does not have a chance to spread is to detect it as early as possible by
using mammography.
In this study, the authors have designed a system that uses a computer to detect
and classify breast cancer on a mammogram image. Mammogram image has been
taken from Mommographic Image Analysis Society (MIAS) which consists of 322
images. Initial processing images on this system using Otsu Thresholding, edge
detection using Canny method, and the method of dilation. Features used in this
system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet
Transform (DWT). Claassification method was used in this study is Support
Vector Machine (SVM).
The system has good resistance to salt and pepper noise on certain noise value for
each type of mammogram image are used. The accuracy range was 80% when
given the noise of -16dB on mammogram images of benign and malignant. The
accuracy of the system was also tested well enough for the amount of training data
that only 70% where the level of detection and classification accuracy is 80,6 %.;Breast cancer is a malignant tumor that grows as a result of the growth of tissue
cells that are not normal in the breast tissue. Breast cancer in women is a disease
that is now the most common cancer than other types. How that is done so that the
disease does not have a chance to spread is to detect it as early as possible by
using mammography.
In this study, the authors have designed a system that uses a computer to detect
and classify breast cancer on a mammogram image. Mammogram image has been
taken from Mommographic Image Analysis Society (MIAS) which consists of 322
images. Initial processing images on this system using Otsu Thresholding, edge
detection using Canny method, and the method of dilation. Features used in this
system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet
Transform (DWT). Claassification method was used in this study is Support
Vector Machine (SVM).
The system has good resistance to salt and pepper noise on certain noise value for
each type of mammogram image are used. The accuracy range was 80% when
given the noise of -16dB on mammogram images of benign and malignant. The
accuracy of the system was also tested well enough for the amount of training data
that only 70% where the level of detection and classification accuracy is 80,6 %., Breast cancer is a malignant tumor that grows as a result of the growth of tissue
cells that are not normal in the breast tissue. Breast cancer in women is a disease
that is now the most common cancer than other types. How that is done so that the
disease does not have a chance to spread is to detect it as early as possible by
using mammography.
In this study, the authors have designed a system that uses a computer to detect
and classify breast cancer on a mammogram image. Mammogram image has been
taken from Mommographic Image Analysis Society (MIAS) which consists of 322
images. Initial processing images on this system using Otsu Thresholding, edge
detection using Canny method, and the method of dilation. Features used in this
system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet
Transform (DWT). Claassification method was used in this study is Support
Vector Machine (SVM).
The system has good resistance to salt and pepper noise on certain noise value for
each type of mammogram image are used. The accuracy range was 80% when
given the noise of -16dB on mammogram images of benign and malignant. The
accuracy of the system was also tested well enough for the amount of training data
that only 70% where the level of detection and classification accuracy is 80,6 %.]

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No. Panggil : T42928
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Subjek :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2015
Program Studi :
Bahasa : ind
Sumber Pengatalogan : LibUI ind rda
Tipe Konten : text
Tipe Media : unmediated ; computer
Tipe Carrier : volume ; online resource
Deskripsi Fisik : xiii, 59 pages : illustration ; 28 cm.
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan Lantai 3
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
T42928 15-18-444738935 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20414358
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