• Sri Widodo Universitas Duta Bangsa Surakarta
  • Mohammad Faizuddin Universiti Kuala Lumpur Malaysia
  • Zalizah Awang Long Universiti Kuala Lumpur Malaysia



ASM, CNN, Morfologi


Kanker paru adalah pertumbuhan sel kanker yang tidak terkendali dalam jaringan paru. Akhir-akhir ini banyak peneliti yang telah menerapkan deep learning, khususnya Convolution Neural Network (CNN) untuk klasifikasi kanker paru. Proses deteksi kanker paru didahului dengan tahapan ekstraksi Region Of Interest (ROI). Ekstraksi ROI dalam deteksi kanker paru terdiri dari dua kegiatan, yaitu segmentasi bidang paru dan operasi segmentasi kandidat kanker paru.  Sebagian besar penelitian tentang deteksi kanker menggunakan CNN, proses ekstraksi ROI dilakukan secara manual dengan melakukan kroping. Proses ini sulit dilakukan, khususnya dalam mensegmentasi bidang paru, yaitu memisahkan area paru dengan jaringan di sekitarnya. Jika kelainan tersebut besar dan terletak pada batas tepi paru, menyebabkan batas tepi paru tidak jelas, sehingga jika dilakukan segmentasi,  citra yang dicurigai sebagai kanker tidak akan masuk dalam citra paru (bagian paru yang terdapat kanker akan hilang). Sehingga segmentasi bidang paru dianggap gagal. Penelitian yang diusulkan bertujuan untuk mengembangkan model  ekstraksi Region Of Interest (ROI) secara otomatis menggunakan metode Active Shape Model dan Mathematical Morphology pada citra CT-Scan. Penelitian yang diusulkan terdiri dari dua tahapan, yaitu, segmentasi bidang paru  menggunakan metode Active Shape Model (ASM) dan segmentasi kandidat paru menggunakan metode Mathematical Morphology.  Hasil segmentasi paru dengan metode Active Shape Model mempunyai akurasi 97,2.8%, sensitifitas 96%, dan spesifisitas 97.4%.  Sedangkan  hasil segmentasi kandidat kanker paru dengan metode marfologi mempunyai akurasi 99,4%, sensitifitas 96,2%, dan spesifisitas 99.7%.


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