Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Project Code :TMMAAI57


In this research article, we have introduced an improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer’s disease


Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. 

Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced an improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease.

 The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, here Multi-texture, grey level features were selected for the analysis. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches.


Keywords: IoMT, classification, deep learning, medical image, features, Crow search algorithm, optimization.

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

Block Diagram


Software & Hardware Requirements:

Software Requirements:

MATLAB R2018a or above

Hardware Requirements:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support.


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended a full installation of all MathWorks products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    •  Morphological processing
    • Segmentation etc.,
  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will be able to know what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:
    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network
  • How to select the features of an object using AI
  • How to extend our work to another real time applications
  • 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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