Skin Cancer Classification Using Random Forest Algorithm

Nurul Khasanah, Rachman Komarudin, Nurul Afni, Yana Iqbal Maulana, Agus Salim

Abstract


Skin cancer is an excessive lump of skin tissue that affects the skin, has an irregular structure with cell differentiation at various levels in chromatin, nucleus and cytoplasm, is expansive, infiltrative to damage the surrounding tissue, and metastasizes through blood vessels and lymph vessels. Diagnosis of skin cancer by biopsy process is considered less effective because it costs a lot and can injure human skin as a sample. For that, we need a system for classification of skin cancer types that are effective and accurate. The application of machine learning has been widely used in the health sector. One of the machine learning methods is Random Forest. In this study, the histogram color feature extraction will be carried out, the hue moment shape extraction, and the haralick texture extraction. Furthermore, the image will be classified using the Random Forest algorithm. The best accuracy value obtained from the histogram feature extraction process and classification with Random Forest is 0.850822. The novelty of this research is the use of more diverse feature extraction and better accuracy results than previous studies. Future research is expected to use deep learning algorithms with CNN (Convolutional Neural Network) architecture to get better accuracy results and add application designs for the application of models that have been formed in the study so that they can be directly applied by the medical team.

Keywords


Skin Cancer; Random Forest Algorithm; Classification of Skin Cancer

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References


Adjed, F., Faye, I., Ababsa, F., Gardezi, S. J., & Dass, S. C. (2016). Classification of Skin Cancer Images Using Local Binary Pattern and SVM Classifier. AIP Conference Proceedings, 1787. https://doi.org/10.1063/1.4968145

Buljan, M., Bulat, V., Situm, M., Mihic, L. L., & Stanic, S. (2008). Variation in Clinical Presentation of Basal Cell Carcinoma. Acta Clin Croat, 47(1), 25–30.

Faruk, M., & Nafi’iyah, N. (2020). Klasifikasi Kanker Kulit Berdasarkan Fitur Tekstur, Fitur Warna Citra Menggunakan SVM dan KNN. Telematika, 13(2), 100–109.

Fibrianda, M. F., & Bhawiyuga, A. (2018). Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(9), 3112–3123.

Firasari, E., Khasanah, N., Khultsum, U., Kholifah, D. N., Komarudin, R., & Widyastuty, W. (2020). Comparation of K-Nearest Neighboor (K-NN) and Naive Bayes Algorithm for the Classification of the Poor in Recipients of Social Assistance. Journal of Physics: Conference Series, 1641(1), 1–6.

Iyatomi, H., Celebi, M. E., Schaefer, G., & Tanaka, M. (2011). Automated color calibration method for dermoscopy images. Computerized Medical Imaging and Graphics, 35(2), 89–98. Retrieved from http://dx.doi.org/10.1016/j.compmedimag.2010.08.003

Lynn, N. C., & Kyu, Z. M. (2018). Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images. Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, 2017-Decem, 117–122. https://doi.org/10.1109/PDCAT.2017.00028

Madhulatha, T. S. (2012). An Overview On Clustering Methods. IOSR Journal of Engineering, 2(4), 719–725.

Mandels, R. J., & Calvin, L. (2014). Tingkat Akurasi Kodefikasi Morbiditas Rawat Inap Guna Menunjang Akurasi Pelaporan Di Bagian Rekam Medis Rumah Sakit Cahya Kawaluyan. Jurnal Kesehatan “Caring and Enthusiasm,” 2(1).

Martin, M., & Nilawati, L. (2019). Recall dan Precision Pada Sistem Temu Kembali Informasi Online Public Access Catalogue (OPAC) di Perpustakaan. Paradigma - Jurnal Komputer Dan Informatika, 21(1), 77–84.

Maskur, & Andriansyah, F. R. (2015). Implementasi Web Semantik Untuk Aplikasi Pencarian Tugas Akhir Menggunakan Ontologi Dan Cosine Similarity. Jurnal Ilmiah NERO, 2(1), 11–18.

Priambodo, H. S., Sari, Y. A., & Widodo, A. W. (2019). Klasifikasi Jenis Citra Makanan menggunakan Color Histogram dan Gray Level Co-occurrence Matrix dengan K-Nearest Neighbour. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(7), 6873–6880.

Seeja, R. D., & Suresh, A. (2019). Melanoma Segmentation and Classification using Deep Learning. (12). https://doi.org/10.35940/ijitee.L2516.1081219

Telaumbanua, F. D., Hulu, P., Nadeak, T. Z., Lumbantong, R. R., & Dharma, A. (2019). Penggunaan Machine Learning Di Bidang Kesehatan. Jurnal Penelitian Teknik Informatika, 2(2), 391–399.

Wilvestra, S., Lestari, S., & Asri, E. (2018). Studi Retrospektif Kanker Kulit di Poliklinik Ilmu Kesehatan Kulit dan Kelamin RS Dr. M. Djamil Padang Periode Tahun 2015-2017. Jurnal Kesehatan Andalas, 7(3), 47–49.




DOI: http://dx.doi.org/10.30700/jst.v11i2.1122

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