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In our Project we implemented on classification method Early detection of Detecting COVID-19is crucial in reducing mortality. Magnetic resonance imaging (MRI) may be a viable imaging technique for Detecting COVID-19detection have been studied for computed tomography (CT) images. However, to the best of our knowledge, no detection methods have been carried out for the MR images. In this paper, a Detecting COVID-19detection method based on deep learning is proposed for thoracic MR images. With parameter optimizing, spatial three-channel input construction, and transfer learning, a faster R-convolution neural network (CNN) is designed to locate the Detecting COVID-19region.


Existing Method:

CT Lung image Classification using

  • Construct concentric multilevel partition
  • Incorporate intensity, texture, and gradient information
  • Image patch feature description
  • Contextual latent semantic analysis-based classifier

Draw Backs:

  • Difficult to get accurate results
  • Not applicable for multiple images for cancer detection in a short time
  • Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification


 Proposed Method:

  • Classification on NN
  • Tensorflowand keras


    Block Diagram:

    Detection of COVID-19 from X-Ray Images


    • It can segment the lung regions from the image accurately.
    • It is useful to classify the lung cancer images for accurate detection.
    • Lung cancer will be detected in an early stages



    • Medical diseases diagnosis system for medical application


     Software Requirements:

    • Python idle
    • Opencv
    • Numpy





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    • Instructor pantech team
    • Duration 15 Hrs
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    • Access 3 Months

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