Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning models for benign and malign Ocular Tumor Growth
  Estimation   Relatively abundant availability of medical imaging data has provided
significant support in the development and testing of Neural Network based
image processing methods. Clinicians often face issues in selecting suitable
image processing algorithm for medical imaging data. A strategy for the
selection of a proper model is presented here. The training data set comprises
optical coherence tomography (OCT) and angiography (OCT-A) images of 50 mice
eyes with more than 100 days follow-up. The data contains images from treated
and untreated mouse eyes. Four deep learning variants are tested for automatic
(a) differentiation of tumor region with healthy retinal layer and (b)
segmentation of 3D ocular tumor volumes. Exhaustive sensitivity analysis of
deep learning models is performed with respect to the number of training and
testing images using 8 eight performance indices to study accuracy,
reliability/reproducibility, and speed. U-net with UVgg16 is best for malign
tumor data set with treatment (having considerable variation) and U-net with
Inception backbone for benign tumor data (with minor variation). Loss value and
root mean square error (R.M.S.E.) are found most and least sensitive
performance indices, respectively. The performance (via indices) is found to be
exponentially improving regarding a number of training images. The segmented
OCT-Angiography data shows that neovascularization drives the tumor volume.
Image analysis shows that photodynamic imaging-assisted tumor treatment
protocol is transforming an aggressively growing tumor into a cyst. An
empirical expression is obtained to help medical professionals to choose a
particular model given the number of images and types of characteristics. We
recommend that the presented exercise should be taken as standard practice
before employing a particular deep learning model for biomedical image
analysis.

20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin