Comprehensive Study of Coronavirus Disease 2019 (COVID-19) Classification based on Deep Convolution Neural Networks

AIRBiS progress meeting†

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Background†

With the Coronavirus disease (COVID-19) disrupting large part of the world, the number of patients has grown progressively. For dealing with this serious emergency, accurate diagnosis and fast reporting are two significant mechanisms.

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Research Goal†

  1. Search and collect X-ray image datasets containing non-infected (normal) and infected (COVID-19) data given many open sources
  2. Perform pre-processing tasks, e.g. as follows:
    1. denoising
    2. resizing
    3. data augmentation
    4. segmentation
    5. we should also come up with other issues and corresponding strategies
  3. CNN implementation and evaluation
    1. A CNN-based architecture will be adopted for the binary classification task. Once the model learns the feature representation of the input images, a classifier can thus output the diagnosis result, i.e., normal or infected. Framework and language: TensorFlow (Keras), Python
    2. Using the diagnosis (classification) results to evaluate the performance of the CNN (e.g., detection accuracy)
AIRBiS-SW.png
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Research Schedule†

MEMO:

Schedule last Updated on: 30/09/2021
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Research Progress†

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References†

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