Research on Medical Image Classification Based on Machine Learning

Project Code :TCPGPY371


In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. A semi supervised learning based image classification method is proposed using Deep Neural Network.


With the increasing demand for faster and more accurate treatment, medical imaging plays an increasingly important role in the early detection, diagnosis and treatment of diseases. Thanks to the development of physics, electronic engineering and computer science and technology, the resolution of medical image is higher and higher, and the image mode is more and more abundant. 

At the same time, the number of medical image is increasing rapidly. At present, X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET / CT), ultrasound imaging are widely used in clinic.The key to achieve accurate diagnosis and treatment is the accurate interpretation of medical images, but the interpretation of images highly depends on the subjective judgment of doctors, so doctors at different levels have great deviation on the results of image interpretation.

Keywords: Generative Adversarial Network; Deep Learning; Feature Extraction; Image Classification

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

Block Diagram



  • Processor: I3/Intel
  • Processor RAM: 4GB (min)
  • Hard Disk: 128 GB
  • Key Board: Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: Any


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

Learning Outcomes

  • Generative adversarial network.
  • Deep learning.
  • Feature extraction.
  • Image classification.
  • Importance of PyCharm IDE.
  • Process of debugging a code.
  • The problem with imbalanced dateset.
  • 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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