Arsitektur U-Net pada Segmentasi Citra Hati sebagai Deteksi Dini Kanker Liver

Authors

  • Therezia Naraloka Sjakhyakirti University
  • Lucky Indra Kesuma Sjakhyakirti University
  • Ade Sukmawati Sjakhyakirti University
  • Marissa Cristianti Sjakhyakirti University

DOI:

https://doi.org/10.33633/tc.v21i4.6669

Keywords:

CNN, U-Net, Segmentation, Liver

Abstract

Hati adalah salah satu organ tubuh manusia yang bertanggung jawab untuk mencerna, meyerap, dan memproses makanan serta berfungsi untuk menyaring darah dari saluran pencernaan sebelum dibawa kebagian organ tubuh lainnya. Hati sangat rentan terhadap berbagai penyakit, salah satunya yaitu kanker liver. untuk itu perlu dilakukannya deteksi sejak dini atau diagnosa terhadap organ hati. Untuk mengatasi permasalahan tersebut, pada penelitian ini dilakukan segmentasi hati menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur U-Net pada citra hati. Langkah awal pada penelitian ini dilakukan pre-processing data yang menerapkan teknik green channel, histogram equalization (HE), dan contrast limited adaptive histogram equalization (CLAHE). Setelah itu dilakukan proses segmentasi sesuai dengan metode yang diusulkan. Penelitian ini menggunakan dataset hati yang didapatkan dari website Kaggle. Hasil penelitian menggunakan metode CNN arsitektur U-Net pada data mendapatkan nilai akurasi sebesar 97,62%, sensitivitas sebesar 89,84%, spesifisitas sebesar 98,37%, koefisen jaccard sebesar 76,99%, dan dice similarity coefficient (DSC) sebesar 87%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode yang diusulkan memiliki hasil yang sangat baik dalam melakukan segmentasi terhadap citra hati.

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Published

2022-11-30

Issue

Vol. 21 No. 4 (2022): November 2022

Section

Articles

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Copyright (c) 2022 Therezia Naraloka, Lucky Indra Kesuma, Ade Sukmawati, Marissa Cristianti

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