Segmentation and Classification of Melanoma Skin Cancer using Deep Learning Techniques

Project Code :TMMAIP378


The main objective of this project is to segment and classify the skin cancer images using morphological operations along with machine learning techniques.


In this work, Skin cancer is the uncontrolled growth of strange skin cells. It occurs when unrepaired DNA damages to skin cells triggers mutations, or genetic defects, that lead the skin cells to multiply readily and form malignant tumors. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin.

The ultimate aim of this paper is to implement cost-effective emergency support systems, to process the medical images. Which are calculated using MATLAB from skin cancer images intending to developing diagnostic algorithms that might improve triage practices in the emergency department.

Here in this project, we will classify skin cancer using Convolutional Neural Networks (CNN).  Experimental results shows that this model is better than Support Vector Machine (SVM) feature classifier a machine learning technique. This classification method proves to be more efficient for the skin cancer classification.

Key words: Image processing, skin cancer, diagnostic algorithms, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Machine Learning.


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

Block Diagram


Software: Matlab 2020a or above


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 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 detect 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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