Deep Convolution Neural Network for Big Data Medical Image Classification

Project Code :TCPGPY377


The objective of this project is to propose a novel deep convolution network-based approach that is assist of doctors and physicians in making reasonable decisions. And to exhibit that this method is best suited to classify various medical images for various body organs.


Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classi?cation, image analysis, clinical archives and object recognition. With an extensive utilization of digital images as information in the hospitals, the archives of medical images are growing exponentially. Digital images play a vigorous role in predicting the patient disease intensity and there are vast applications of medical images in diagnosis and investigation.

Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. For classifying the medical images according to their relevant classes a most suitable classi?er is most important. Where we are proposing our model where the algorithm is trained for classifying medical images by deep learning technique. A pre-trained deep convolution neural network (GoogleNet) is used that which can classifies the various medical images for various body organs. This method of image classi?cation is bene?cial to predict the appropriate class or category of unknown images. The results of the experiment exhibit that our method is best suited to classify various medical images. 

Keywords: Medical image classi?cation, pre-trained DCNN, convolution neural network, deep learning

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: 8GB (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, Numpy, Flask, IO, OS

Learning Outcomes

  • Error correction mechanisms
  • What type of technology versions is used?
  • Working of Tensor Flow
  • Implementation of Deep Learning techniques
  • Working of CNN algorithm
  • Working of GoogleNet algorithm
  • Building of model creations
  • Scope of project
  • Applications of the project
  • About Python language
  • About Deep Learning Frameworks
  • Use of Data Science
  • Practical exposure 
    • Hardware and software tools
    • Solution providing for real time problems
    • Working with team/individual
    • Work on creative ideas

Demo Video

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