Discriminative Feature Learning for Thorax Disease Classification in Chest X-Ray Images

Project Code :TCMAPY568

Objective

The main objective of this Project is to create an effective system for classifying Thorax diseases using chest x-ray images using dense net architecture.

Abstract

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this project, we used transfer learning technique, named Dense net, to achieve our target prediction and also used to improve the performance of thorax disease classification in CXRs. Experiments conducted on the thorax disease dataset demonstrate the effectiveness of the proposed method.

Keywords: Classification, Thorax disease, Dense Net, Transfer Learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

  • Processor: I3/Intel
  • Processor RAM: 8GB (min)
  • Hard Disk: 128 GB

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Server-side Script: Python 3.6+
  • IDE: PyCharm
  • Libraries Used: Pandas, Numpy, Flask, TensorFlow, Keras.

Learning Outcomes

  • About Python.
  • About Jupyter Notebook.
  • About Pandas.
  • About Numpy.
  • About HTML.
  • About CSS.
  • About JavaScript.
  • About Database.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Cloud Overview.
  • Terminology of cloud.
  • 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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