Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16

REGITA AGUSTINA, RITA MAGDALENA, NOR KUMALASARI CAECAR PRATIWI

Sari


ABSTRAK

Kanker kulit merupakan penyakit yang ditimbulkan oleh perubahan karakteristik sel penyusun kulit dari normal menjadi ganas, yang menyebabkan sel tersebut membelah secara tidak terkendali dan merusak DNA. Deteksi dini dan diagnosis yang akurat diperlukan untuk membantu masyarakat mengindentifikasi apakah kanker kulit atau hanya kelainan kulit biasa. Pada studi ini, dirancang sebuah sistem yang dapat mengklasifikasi kanker kulit dengan memanfaatkan citra kulit pasien yang kemudian diolah menggunakan metode Convolutional Neural Network (CNN) arsitektur VGG-16. Dataset yang digunakan berupa citra jaringan kanker sebanyak 4000 gambar. Proses diawali dengan input citra, pre-processing, pelatihan model dan pengujian sistem. Hasil terbaik diperoleh pada pengujian tanpa pre-processing CLAHE dan Gaussian filter, dengan menggunakan hyperparameter optimizer SGD, learning rate 0,001, epoch 50 dan batch size 32. Akurasi yang diperoleh sebesar 99,70%, loss 0,0055, presisi 0,9975, recall 0,9975 dan f1-score 0,9950.

Kata kunci: Kanker kulit, CNN, VGG-16

 

ABSTRACT

Skin cancer is a disease caused by changes in the characteristics of skin cells from normal to malignant, which causes the cells to divide uncontrollably and damage DNA. Early detection and accurate diagnosis are necessary to help the public identify whether skin cancer or just a common skin disorder. In this study, a system was designed that can classify skin cancer by utilizing images of patients' skin which is then processed using the Convolutional Neural Network (CNN) method of VGG-16 architecture. Dataset used in the form of cancer tissue imagery as many as 4000 images. The process begins with image input, pre-processing, model training and system testing. The best results were obtained on testing without pre-processing CLAHE and Gaussian filters, using hyperparameters, SGD optimizer, learning rate 0.001, epoch 50 and batch size 32. Accuracy obtained by 99.70%, loss 0.0055, precision 0.9975, recall 0.9975 and f1-score 0.9950.

Keywords: Skin cancer, CNN, VGG-16


Kata Kunci


Kanker kulit; CNN; VGG-16

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i2.446

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