Hierarchical Ensemble Learning untuk Klasifikasi Tumor Payudara pada Citra Ultrasonografi (USG)

Rahmad Hidayat, Muhammad Davi, Hanafi Hanafi

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Kanker payudara merupakan jenis kanker yang menempati urutan pertama terbanyak di indonesia, sekaligus sebagai penyumbang kematian akibat kanker tertinggi. Berdasarkan fata Globocan tahun 2020, jumlah kasus baru kanker payudara mencapai 68.858 kasus (16,6%) dari total 396.914 kasus baru kanker di Indonesia. Sementara itu, untuk jumlah kematiannya mencapai lebih dari 22 ribu jiwa kasus. Deteksi dini merupakan salah satu faktor penting yang dapat mengurangi rasio mortalitas pasien sampai dengan 43%. Pada tahun 2023, menggunakan dana DIPA PNL pengusul telah melakukan penelitian yang mengembangkan model untuk mendeteksi kanker payudara pada citra Ultrasonografi (USG). Model yang dikembangkan pada penelitian tersebut melakukan ekstraksi fitur tekstur yaitu Gabor dan GLCM (Gray Level Co-occurrence Matrix) dari citra USG payudara. Berdasarkan hasil pengujian didapatkan akurasi sebesar 0.67 (training) dan 0.66 (validasi). sementara itu, loss yang didapat adalah sebesar 0.77 saat training dan 0.84 saat validasi. Akurasi yang didapat pada model tersebut dipandang belum memuaskan sehingga diperlukan pengembangan lebih lanjut. Pada usulan penelitian ini, pengusul mengembangkan model yang melatih beberapa klasifier yaitu Support Vector Machine (SVM), Naïve Bayes, dan Random Forest. Hasil klasifikasi pada satu arsitektur kemudian diinputkan pada klasifier berikutnya secara hierarkis. Hasil akurasi yang didapatkan setelah menambahkan tahapan ensemble meningkat yaitu sebesar 0,96 dan AUC sebesar 0,94.


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