Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network

  • Luqman Hakim Universitas Muhammadiyah Malang
  • Zamah Sari Universitas Muhammadiyah Malang
  • Handhajani Handhajani Universitas Gajayana Malang
Keywords: CNN, skin cancer, convolution., Neural Network, Deep Learning

Abstract

Skin cancer is a very common form of cancer that can be found in the United States with annual treatment costs exceeding $ 8 billion. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to help the medical and medical world in analyzing images accurately and accurately. Researchers propose to classify skin cancer pigments by focusing on two classes, namely non-melanocytic malignant and benign, where the skin cancer category which is classified into the non-melanocytic class is Actinic keratoses, Basal cell carcinoma. While skin cancers that are classified into Benign are Benign keratosis like lesions, dermatofibrama, vascular lessions. The method used in this study is Convolutional Neural Network (CNN) with a model architecture using 8 Convolutional 2D layers which have filters (16, 16, 32, 32, 64, 64, 128, 128). The first input layers are (20,20). and the following layers (5,5 and 3,3), the types of pooling used in this study are MaxPooling and AveragePooling. The Fully Connected Layer used is (256, 128) and uses a Dropout (0.2). The dataset is obtained from the International Skin Imaging Collaboration (ISIC) 2018 with a total of 10015 images. Based on the results of the test and evaluation reports, an accuracy of 75% is obtained. with the highest precision and recall values ​​found in the Benign class, namely 0.80 and 0.82 respectively and the f1_score value of 0.81.

 

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Published
2021-04-29
How to Cite
Luqman Hakim, Sari, Z., & Handhajani, H. (2021). Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 379 - 385. https://doi.org/10.29207/resti.v5i2.3001
Section
Artikel Teknologi Informasi