Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Study and development of a Computer-Aided Diagnosis system for
  classification of chest x-ray images using convolutional neural networks
  pre-trained for ImageNet and data augmentation   Convolutional neural networks (ConvNets) are the actual standard for image
recognizement and classification. On the present work we develop a Computer
Aided-Diagnosis (CAD) system using ConvNets to classify a x-rays chest images
dataset in two groups: Normal and Pneumonia. The study uses ConvNets models
available on the PyTorch platform: AlexNet, SqueezeNet, ResNet and Inception.
We initially use three training styles: complete from scratch using random
initialization, using a pre-trained ImageNet model training only the last layer
adapted to our problem (transfer learning) and a pre-trained model modified
training all the classifying layers of the model (fine tuning). The last
strategy of training used is with data augmentation techniques that avoid over
fitting problems on ConvNets yielding the better results on this study

6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg