Autors: Iliyan Kordev I.K., Borovska, B. P.
Title: Design and implementation of differentiated analytics workflow for imaging diagnostics on the intelligent integrated digital platform InSilicoKDD
Keywords: deep convolutional neural networks, scientific workflow for imaging diagnostics, abnormal pneumonias

Abstract: In this paper we have proposed a conceptual model of differential analytical scientific workflow for imaging diagnostics of abnormal pneumonias. The model is based on the method of deep convolutional neural networks (CNN) and the approach of Gradient-weighted Class Activation Mapping (Grad-CAM), which uses specific for the class information from the gradient, incoming in the last convolutional layer of CNN, in order to create rough map of localization of important region within the medical image. The proposed conceptual model is implement in Python with Google’s open source framework Tensorflow and Keras. In the development process, we have used customized Jupiter Notebook – Colab. For the creation and visualization plots we used Matplotlib. Neural Networks are pre-trained with ImageNet dataset, which consist of large amount of non-medical images. The experimental results for the 5 types of convolutional neural networks show accuracy more than 95%.

References

  1. Iliyan Kordev, Plamenka Borovska, 1950, Design and implementation of differentiated analytics workflow for imaging diagnostics on the intelligent integrated digital platform InSilicoKDD, AIP Conference Proceedings, Volume 2333, pp. 030006 1-7

Issue

AIP Conference Proceedings, vol. 2333, pp. 030006 1-7, 2021, Bulgaria, AIP Publishing, https://doi.org/10.1063/5.0043740

Copyright AIP Publishing

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Вид: публикация в международен форум, публикация в издание с импакт фактор