LUNG CANCER DETECTION BY HYBRID LEARNING METHOD APPLYING SMOTE TECHNIQUE

Authors : Alihan SUİÇMEZ, Çağrı SUİÇMEZ, Cengiz TEPE

Pages : 1098-1110

Doi:10.29109/gujsc.1201819

View : 14 | Download : 14

Publication Date : 2022-12-30

Article Type : Research Paper

Abstract :Lung cancer is a very deadly disease. However, early diagnosis and detection is an essential factor in overcoming this deadly disease. Tumors formed in this disease\`s initial stage are divided into benign and malignant. These can be visualized using a computed tomography insert ignore into journalissuearticles values(CT); scan. Thanks to machine learning and deep learning, cancer stages can be detected using these images. In our study, the best and most promising results in the literature were obtained by using a hybrid learning architecture. The data mining techniques we use in obtaining these results also play a significant role. The best accuracy result we obtained belongs to the CNN+GBC hybrid algorithm, which we recommend with 99.71%.
Keywords : Lung cancer detection, deep learning, hybrit learning, classification