  In this paper, we examine the strength of deep learning technique for
diagnosing lung cancer on medical image analysis problem. Convolutional neural
networks (CNNs) models become popular among the pattern recognition and
computer vision research area because of their promising outcome on generating
high-level image representations. We propose a new deep learning architecture
for learning high-level image representation to achieve high classification
accuracy with low variance in medical image binary classification tasks. We aim
to learn discriminant compact features at beginning of our deep convolutional
neural network. We evaluate our model on Kaggle Data Science Bowl 2017 (KDSB17)
data set, and compare it with some related works proposed in the Kaggle
competition.
