Lung cancer is the leading cause of cancer deaths in the world and early detection is a crucial part of increasing patient survival. Deep learning techniques provide us with a method of automated analysis of patient scans. In this work, we compare AlexNet, a multi-layered and highly flexible architecture, with a custom CNN to determine if lung nodules with patient scans are benign or cancerous. We have found our CNN architecture to be highly accurate (99.79%) and fast while maintaining low False Positive and False Negative rates (< 0.01% and 0.15% respectively). This is important as high false positive rates are a serious issue with lung cancer diagnosis. We have found that AlexNet is not well suited to the problem of nodule identification, though it is a good baseline comparison because of its flexibility.
The authors compare a standard DL machine (AlexNet) with a custom CNN-based solution in the well known tasks of classifying lung tumours into benign or cancerous in the Luna CT scan dataset, concluding that the proposed novel solution performs better.
The paper is interesting, but it has a number of issues that prevents it from being accepted for the ICLR conference.
First, the scope of the paper, in its present form, is very limited: the idea of comparing the novel solution just with AlexNet is not adding much to the present landscape of methods to tackle this problem.
Moreover, although the task is very well known and in the last few year gave rise to a steady flow of solutions and was also the topic of a famous Kaggle competition, no discussion about that can be found in the manuscript.
The novel solution is very briefly sketched, and some of the tricks in its architecture are not properly justified: moreover, the performance improvement w.r.t . to AlexNet is hardly supporting the claim.
Experimental setup consists of just a single training/test split, thus no confidence intervals on the results can be defined to show the stability of the solution.
The whole sections 2.3 and 2.4 include only standard material unnecessary to mention given the target venue, and the references are limited and incomplete.
This given, I rate this manuscript as not suitable for ICLR 2018.