Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform

Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah

doi:10.54914/jtt.v9i1.605

Penulis

Simeon Yuda Prasetyo Universitas Bina Nusantara
Ghinaa Zain Nabiilah Universitas Bina Nusantara

DOI:

https://doi.org/10.54914/jtt.v9i1.605

Kata Kunci:

DCT, Klasifikasi Biner, Machine Learning, SVM, Tumor Otak

Abstrak

Tumor otak adalah pertumbuhan jaringan abnormal yang ditandai dengan pertumbuhan sel yang berlebihan di bagian otak tertentu. Salah satu teknik andal saat ini yang tersedia untuk mengidentifikasi tumor otak adalah penggunaan pemindaian Magnetic Resonance Imaging (MRI). Gambar MRI yang dipindai dipantau dan diperiksa untuk deteksi tumor oleh dokter spesialis. Mengembangkan alat yang lebih efektif dan efisien untuk membantu profesional medis mengidentifikasi tumor otak dirasa cukup mendesak karena jumlah orang yang menderita tumor otak melonjak dan tingkat kematian yang mencapai 18.600 pada tahun 2021. Dalam penelitian sebelumnya, model berbasis pembelajaran mesin mampu menunjukkan kemampuan untuk mendeteksi tumor otak dengan akurasi klasifikasi 92% dan hal hasil ini dapat diandalkan. Untuk mendapatkan akurasi klasifikasi biner yang paling andal dalam gambar otak MRI, kami menguji secara komputasi beberapa hyperparameter menggunakan kumpulan data MRI yang tersedia untuk umum. Tingkat akurasi model yang tinggi dicapai dengan menguji berbagai arsitektur model machine learning yang ada, diikuti dengan memasukkan feature map yang diekstraksi dari Discrete Cosine Transform (DCT). Klasifikasi gambar MRI mencapai akurasi pada data test tertinggi sebesar 93% dengan menggunakan model Support Vector Machine (SVM).

Unduhan

Data unduhan belum tersedia.

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