Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurence Matrix (GLCM)

https://doi.org/10.22146/ijccs.3496

Refta Listia(1*), Agus Harjoko(2)

(1) 
(2) Jurusan Ilmu Komputer dan Elektronika (JIKE), Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta
(*) Corresponding Author

Abstract


Abstrak

Kanker payudara adalah penyakit yang paling umum dideritaoleh wanitapadabanyak negara. Pemeriksaan kanker payudara dapat dilakukan dengan menggunakan mamografi.Padapenelitianini, pendekatan yang diusulkan bertujuanuntuk mengklasifikasi mammogram berdasarkan tiga kelas yaitukelas normal, tumor jinak, dan tumor ganas. Sistem yang diusulkan terdiri dari empat langkah utamayaitu preprosesing, segmentasi, ekstraksi fitur dan klasifikasi. Padatahappreprosesingakandilakukangrayscale, interpolasi, amoeba mean filter dan segmentasi. Ekstraksi ciri menggunakan Gray Level Cooccurence Matrix (GLCM) danakan dihitung ciri-ciristatistikpada 4 arah (d=1 dan d=2) , GLCM 8 arah(d=1) dan GLCM 16 arah (d=2).Fitur yang digunakanada 5 yaitukontras, energi, entropi, korelasi dan homogenitas. Langkah terakhir adalah klasifikasi menggunakan Backpropagation. Beberapa parameter penting divariasikan dalam proses ini seperti learning rate dan jumlah node dalam lapisan tersembunyi. Hasil penelitian menunjukkan bahwa fitur ekstraksi GLCM 4 arah(denganjarak d=1memiliki akurasi terbaik dalammengklasifikasimammogram yaitusebesar 81,1% dankhususpadaarah akurasi klasifikasidiperolehsebesar 100%.

 

 

Abstract

Breast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps : preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2),  GLCM 8 angles and GLCM 16 angles.  The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in  hidden layer. The research result suggest that extraction feature in 4 angles ( and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in accuracy is 100%.


Keywords


mammogram, GLCM, Backpropagation

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References

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DOI: https://doi.org/10.22146/ijccs.3496

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