About: In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classification tasks using transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is in smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset despite being computationally less expensive.   Goto Sponge  NotDistinct  Permalink

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  • In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classification tasks using transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is in smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset despite being computationally less expensive.
subject
  • Pulmonology
  • Machine learning
  • Classification algorithms
  • Thorax (human anatomy)
  • Projectional radiography
  • Datasets in computer vision
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