On Urinary Bladder Cancer Diagnosis: Utilization of Deep Convolutional Generative Adversarial Networks for Data Augmentation
, Sandi Baressi Šegota 1,*, Nikola Anđelić 1, Vedran Mrzljak 1, Tomislav Ćabov 2, Josip Španjol 3,4 and Zlatan Car 1Round 1
Reviewer 1 Report
In this manuscript, authors describe a very comprehensive computational analysis- a machine learning strategy- on urinary bladder cancer diagnosis. The manuscript is overall well written. Especially, the authors paid too much attention for the presenting of the methods section which I highly appreciate in this manuscript. From my point of view, the manuscript is acceptable only with some minor corrections:
- Line 22 and 23: AlexNet is differently written. Please make it consistent
- Line 61: CNN is first time used, please write it
- Line 71: AI is first time used
- Line 81: ANN is first time used
- Line 94: data set -> change it as dataset
- Line 129: image 3 -> Image (please control whole manuscript, equations, tables and images must be start with capital letter)
- In Equation 1: please refer the parameters n and k
- Line 160: equation 4 -> Equation 4 -> e capital
- new matrix C(x, h) is performed using equation 3 -> e capital
- Line 163: The networks in question -> Equation number is missing and e capital
- Lines 205 and 207: equation -> e capital
- Line 249: subfigure 5a -> change it as Figure 5a (do same changes please for the following lines as well )
Author Response
Respected reviewer,
please find answers to your comments in the attached PDF.
Kindest regards
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This is an interesting article aimed at investigating the influence of Generative Adversarial Networks (GAN)-based image data augmentation of CNNs for urinary bladder cancer diagnosis. The paper is well written and the data well presented. I have only some minor comments:
- the abstract is too long
- Figure 2. Representation of all four cystoscopy dataset classes. Please add a scale bar and provide a description of these images in the text.
Author Response
Respected reviewer,
please find the answers to the comments you posed attached.
Kindest regards,
Authors
Author Response File: Author Response.pdf
