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HemorrhageDetection
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rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly





  
  

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trained specialists analyzing a Computed tomography (CT)">





  
  

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rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly





  
  

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trained specialists analyzing a Computed tomography (CT)">





  
  

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        <h5 class="u-text u-text-body-alt-color u-text-1"> Artificial intelligence powered&nbsp;</h5>





  
  

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        <h1 class="u-text u-text-body-alt-color u-text-2" spellcheck="false">&nbsp;intracranial hemorrhage&nbsp;<br>detection





  
  

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        <h5 class="u-text u-text-1"> Intracranial hemorrhage detection using neural network based methods</h5>





  
  

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        <h3 class="u-text u-text-2" spellcheck="false">abstract</h3>





  
  

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        <p class="u-text u-text-grey-40 u-text-3" spellcheck="false"> Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring<br>rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly<br>trained specialists analyzing a Computed tomography (CT) scan of the patient and identifying the<br>location and type of hemorrhage if one exists. When a patient shows acute neurological symptoms such<br>as severe headache or loss of consciousness, highly trained specialists review medical images of the<br>patient’s cranium to look for the presence, location and type of hemorrhage. The process is complicated<br>and often time consuming as these scans are essentially a combination of multiple X-Ray scans<br>processed by a computer. To reduce delays which lead to deaths we propose a solution to this problem<br>based on a Machine Vision based approach to automate the detection of intracranial hemorrhaging. We<br>employ a modified version of ResNet 34 trained on a dataset provided by Radiological Society of North<br>America. These convolutional networks are then stacked using a sequence model (such as a recurrent<br>neural network) to preserve temporal information. The system can detect acute intracranial hemorrhage<br>and its subtypes with inference times less than 1 second and accuracy greater than 85%. We aim to<br>provide automated, faster and more accurate diagnosis using computer vision and deep residual learning<br>frameworks for image recognition and classification





  
  

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        <a href="#" class="u-border-2 u-border-white u-link u-no-underline u-text-body-alt-color u-link-1">read more</a>





  
  

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                  <h5 class="u-custom-font u-font-roboto u-text u-text-1"> Problem Statement</h5>





  
  

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                  <h1 class="u-custom-font u-font-roboto u-text u-text-2" spellcheck="false">fast accurate inference<br>





  
  

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                  <p class="u-text u-text-3" spellcheck="false"> The bottleneck , caused by human limitations is lessened by adding the deep learning model.<br>The time spent by the specialist is reduced due to the information provided by the model.<br>Summary reports can be shared digitally for verification as well in extreme cases





  
  

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                  <p class="u-text u-text-4" spellcheck="false"> The raw data never leaves the institutions, which not only<br>adds privacy and reinforce patient confidentiality but also<br>prevents large data transfers on the network.





  
  

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                          <p class="u-text u-text-2" spellcheck="false">INTRACRANIAL HEMORRHAGE DETECTION</p>





  
  

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                          <h1 class="u-text u-text-3" spellcheck="false">the model</h1>





  
  

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                          <p class="u-text u-text-palette-2-dark-2 u-text-4" spellcheck="false"> Our work here aims to reduce the computational<br>load of the model via pruning and repurposing the architecture<br>to create a more practical model for inference at even remote<br>locations with limited computation power.<br>





  
  

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                            <br> We employ a modified version of DenseNet121&nbsp; trained<br>on a dataset provided by the Radiological Society of North<br>America. These convolutional networks are then stacked using<br>a sequence model (such as a recurrent neural network) With<br>GRUs to preserve temporal information. The system can<br>detect acute intracranial hemorrhage and its subtypes with<br>accuracy greater than 92%. We aim to provide automated,<br>faster, and more accurate diagnoses using computer vision<br>and deep learning frameworks for image recognition and<br>classification.





  
  

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                          </p>





  
  

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                          <a href="#" class="u-border-3 u-border-black u-link u-no-underline u-text-black u-link-1">learn more</a>





  
  

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        <h2 class="u-text u-text-1" spellcheck="false">MRI -&gt; INFERENCE</h2>





  
  

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        <p class="u-text u-text-2" spellcheck="false">Our AI-powered model can be integrated with hospital networks to provide lightening fast inferences to save lives</p>





  
  

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                    <p class="u-text u-text-2" spellcheck="false"> Our work here aims to reduce the computational<br>load of the model via pruning and repurposing the architecture<br>to create a more practical model for inference at even remote<br>locations with limited computation power.





  
  

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                    <p class="u-text u-text-4" spellcheck="false"> The raw data never leaves the institutions, which not only<br>adds privacy and reinforce patient confidentiality but also<br>prevents large data transfers on the network





  
  

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        <p class="u-large-text u-text u-text-default u-text-variant u-text-2"> The data from the hospitals can be extremely skewed,<br>depending on the specialty of the doctors in question.<br>This works in our favor as it allows us to take advantage<br>of the federated passes and better personal inference<br>scenarios.





  
  

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                  <h2 class="u-text u-text-default u-text-1" spellcheck="false">Contact Us</h2>





  
  

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                  <p class="u-text u-text-2" spellcheck="false">to learn more about our research please contact tkrsh.srv.09@gmail.com</p>





  
  

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