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

3 Jun 2018  ·  Vinicius Pavanelli Vianna ·

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 read more

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.