DETECTION OF LUNG CANCER CELL BASED ON CYTOLOGICAL EXAMINATION USING CONVOLUTIONAL NEURAL NETWORK
Abstract
Lung cancer is one of the most dangerous cases with the largest number of new cases in the world. The number of Lung Cancer in Indonesia is increasing rapidly every day until it is ranks 8th position in Southeast Asia, experiencing an increase in the last five years by 10.85 percent. This study aims to build a tool to detect lung cancer using the Deep Learning classification method with the Convolutional Neural Network (CNN) Algorithm. The tools that are made can be used for consideration in detecting from the results of cytological examinations, can be classified into normal (negative) and abnormal (positive) types of cancer. The experiment was carried out by performing hyperparameter optimization. The results show that the hyperparameter optimization has superior results compared to others, using the hyperparameter Gradient Boosted Regression Tree method. Experiments without hyperparameters give an accuracy value of 97%, while with the Gaussian Process it gives 98% accuracy and with a hyperparameter gradient boosted regression tree gives 99% accuracy, which is the best accuracy.
Keywords : Lung Cancer, Cytological Examinations, Deep Learning, Convolutional Neural Network (CNN)
terbanyak di dunia. Jumlah penderita Kanker Paru di Indonesia semakin hari semakin meningkat pesat hingga menduduki urutan ke-8 di Asia Tenggara, mengalami peningkatan dalam lima tahun terakhir sebanyak 10.85 persen. Penelitian ini bertujuan untuk membangun alat pendeteksi kanker paru menggunakan metode klasifikasi Deep Learning dengan Algoritma Convolutional Neural Network (CNN). Alat yang dibuat dapat digunakan sebagai pertimbangan dalam mendeteksi Kanker Paru dari hasil pemeriksaan sitologi, diklasifikasikan menjadi jenis normal (negatif) dan abnormal (positif) kanker. Percobaan dilakukan dengan melakukan optimasi hyperparameter. Hasil penelitian menunjukkan bahwa optimasi hyperparameter memiliki hasil yang lebih unggul yaitu dengan menggunakan metode hyperparameter Gradient Boosted Regression Tree. Percobaan tanpa hyperparameter memberikan nilai akurasi 97%, sedangkan dengan Gaussian Process memberikan akurasi 98% dan dengan hyperparameter Gradient Boosted Regression Tree memberikan akurasi terbaik yaitu 99%.
Kata Kunci : Kanker Paru, Pemeriksaan Sitologi, Deep Learning, Convolutional Neural Network (CNN)
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DOI: http://dx.doi.org/10.20527/klik.v7i3.344
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