Analisis Berbasis Convolutional Neural Network untuk Pendeteksian Kanker Prostat dengan Citra Magnetic Resonance Imaging (MRI)

Fikri Yoma Rosyidan; Rimuljo Hendradi; Eto Wuryanto

doi:10.30591/jpit.v10i3.8397

Analisis Berbasis Convolutional Neural Network untuk Pendeteksian Kanker Prostat dengan Citra Magnetic Resonance Imaging (MRI)

Fikri Yoma Rosyidan, Rimuljo Hendradi, Eto Wuryanto

Abstract

Kanker prostat adalah tumor ganas yang berada dari kelenjar prostat, yang merupakan bagian penting dari sistem reproduksi pria. Adanya peningkatan prevalensi kanker prostat maka diperlukan deteksi dini yang akurat. Penelitian ini memfokuskan pada pemanfaatan deep learning, khususnya metode Convolutional Neural Network (CNN) untuk mendiagnosis kanker prostat melalui citra MRI. Diperlukan penelitian untuk mengkaji tiga arsitektur CNN: U-Net, nnU-Net, dan nnDetection agar didapatkan arsitektur yang terbaik. Data penelitian ini menggunakan data sekunder sejumlah 1294 citra MRI dari The PI-CAI Challenge “Artificial Intelligence & Radiologists Prostate Cancer Detection in MRI” tahun 2022. Data tersebut menjalani proses pre-processing, termasuk normalisasi intensitas piksel, augmentasi data seperti rotasi dan scaling, serta pemotongan gambar untuk menghilangkan area yang tidak relevan. Proses selanjutnya data tersebut akan masuk ke tahap pelatihan model dengan menggunakan ketiga arsitektur. Hasil dari pelatihan tersebut akan dievaluasi kinerja modelnya dengan menggunakan metrik Area Under the Receiver Operating Characteristic Curve (AUROC) dan Average Precision (AP). Hasil evaluasi menunjukkan bahwa arsitektur U-Net mencapai AUROC 89,94% dan AP 51,22%, arsitektur nnU-Net mencapai AUROC 97,75% dan AP 86,67%. dan arsitektur nnDetection mencapai AUROC 83,66% serta AP 49,91%. Dari perbandingan hasil ketiga arsitektur maka didapatkan hasil terbaik adalah arsitektur nnU-Net dengan capaian AUROC 97,75% dan AP 86,67%. Penelitian ini menunjukkan potensi penggunaan CNN dalam diagnosis kanker prostat melalui citra MRI. Temuan penelitian menegaskan pentingnya pemilihan arsitektur yang tepat dalam aplikasi deep learning untuk citra medis.

Keywords

AUROC, AP, Convolutional Neural Network (CNN), prostate cancer

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DOI: https://doi.org/10.30591/jpit.v10i3.8397