Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma Convolutional Neural Network; Image Classification; Melanoma Classification; DenseNet121.

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Jasman Pardede
Dwi Adi Lenggana Putra

Abstract

Skin is a part of a human body that covers the entire body and protect the lower layer from direct sunlight and another microorganism. Because of that, skin cells are always changing and could be changed because of genetic mutation that causes skin cancer. In general, skin cancer is divided into three groups, namely : skin cancer Basal cell carcinoma, skin cancer Squamous cell carcinoma, and skin cancer Melanoma. Melanoma skin cancer is caused by abnormal growth in melanocyte cells. Several methods are proposed to predict Melanoma skin cancer using  ResNet, LeNet, and Support Vector Machine. System performance is measured based on the value of accuracy, precision, recall, and f-measure. This experiment is conducted using a Melanoma skin cancer dataset that obtained the average value in terms of accuracy, precision, recall, and f-measure are 0.94, 0.95, 0.92, and 0.94 respectively. Based on that result, the proposed DenseNet121 performs better with 0.94 accuracy, compared with ResNet, LeNet, and Support Vector Machine method.
 
Keywords— Convolutional Neural Network; Image Classification; Melanoma Classification; DenseNet121.

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How to Cite
Pardede, J., & Putra, D. (2020). Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma. JuTISI (Jurnal Teknik Informatika Dan Sistem Informasi), 6(3). https://doi.org/10.28932/jutisi.v6i3.2814
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