Paper PublishingDeep learning for Classification and Localization of COVID-19 Markers in point-of-care Lung Ultrasound
Objective
Based on lung ultrasonography (LUS) images our app should identify whether a person got infected with CORONAVIRUS, based on which medication should be recommended. Such an app could be helpful in detecting CORONAVIRUS faster and hence faster treatment can be provided to the user.
Abstract
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID19 pandemic, some studies have started to investigate DL based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this project studies the application of DL techniques for the analysis of lung ultrasonography images.
Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video level, and pixel-level. In proposed system a deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to input frame and provides localization of pathological artefacts in a weakly-supervised way. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers.
Keywords: COVID-19, Lung Ultrasound, Deep Learning, CT scan.
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Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy, sklearn, Flask, TensorFlow, OS.
Learning Outcomes
- Scope of real time application scenarios.
- How Internet Works.
- How to create database.
- Gathering requirements related to project.
- What type of technology versions?
- Use of HTML and CSS on UI Designs.
- Data Base Connections.
- Data Parsing Front-End to Back-End.
- Need of PyCharm-IDE to develop a web application.
- Working Procedure.
- Testing Techniques.
- Increasing accuracy of a model.
- Error Correction mechanisms.
- How to run and deploy the applications.
- Introduction to basic technologies.
- How project works.
- Input and Output modules.
- How test the project based on user inputs and observe the output.
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
