Comparison of lung cancer detection algorithms - GÜNAY, MELİKE; GÜNAYDIN, ÖZGE; ŞENGEL, ÖZNUR (International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT), 2019)
Lung cancer is a kind of difficult to diagnose and dangerous cancer. It commonly causes death both men and women so fast accurate analysis of nodules is more important for treatment. Various methods have been used for detecting cancer in early stages. In this paper, machine learning methods compared while detect lung cancer nodule. We applied Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Decision Trees and Artificial Neural Networks machine learning methods to detect anomaly. We compared all methods both after preprocessing and without preprocessing. The experimental results show that Artificial Neural Networks gives the best result with 82,43% accuracy after image processing and Decision Tree gives the best result with 93,24% accuracy without image processing.
Keywords: Lung Cancer; Classification; Machine Learning; Artificial Neural Networks; Support Vector Machines; Decision Trees; Naive Bayes; Akciğer Kanseri; Sınıflandırma; Makine Öğrenme; Yapay Sinir Ağları; Vektör Makineleri Desteklemek; Karar Ağaçları