Abstract:
In the race of all cancerous diseases, lung cancer is in the first place. Every year lots of
people died because of cancer and lung cancer is playing the leading role among them. In
the year of 2018, 9.6 million people died because of cancer where 1.76 million death
occurred due to lung cancer. In this study, we experiment with a deep learning model with
kNN classifier to extend the success rate in diagnosing lung cancer. The dataset used in
this study is a publicly accessible resource SPIE-AAPM. We used data augmentation on
the training dataset to expand the dataset and convolutional neural network (CNN) to
extract the related features. Extracted features from CNN used as input to the kNN
classifier with cross-validation. The experiment hit accuracy of 90% by predicting the
dataset with the help of selected features and kNN classifier.
