Skin Cancer Classification using Image Processing and Machine Learning

Project Code :TMMAAI237


The objective of the project is to develop an accurate and efficient system for the detection and classification of skin cancer from images using advanced image processing techniques and machine learning algorithms. The project aims to create a reliable and automated tool that can assist dermatologists and healthcare professionals in the early detection and diagnosis of skin cancer, leading to improved patient outcomes and reduced healthcare costs.


One of the most rapidly spreading cancers among various other types of cancers known to humans is skin cancer. Melanoma is the worst and the most dangerous type of skin cancer that appears usually on the skin surface and then extends deeper into the layers of skin. However, if diagnosed at an early stage; the survival rate of Melanoma patients is 96% with simple and economical treatments. The conventional method of diagnosing Melanoma involves expert dermatologists, equipment, and Biopsies. To avoid the expensive diagnosis, and to assist dermatologists, the field of machine learning has proven to provide state of the art solutions for skin cancer detection at an earlier stage with high accuracy. In this paper, a method for skin lesion classification and segmentation as benign or malignant is proposed using image processing and machine learning.

Keywords: Skin lesion segmentation, contrast stretching, features extraction, features reduction, features normalization, features scaling, wrapper method, skin cancer classification, random forest classifier.

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

Block Diagram


Software and hardware requirements: 

Software: MATLAB 2020a or above

Hardware: 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 Math Works products may take up to 29 GB of disk space 


Minimum: 4 GB 

Recommended: 8 GB

Learning Outcomes

  • Introduction to MATLAB
  • What are EISPACK and LINPACK?
  • 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.,
  • 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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