Segmentation and Classification of Melanoma Skin Cancer using CNN
Objective
This study examines deep learning-based melanoma diagnosis, emphasizing CNNs for segmenting and classifying skin cancer. Pre-processing uses image resizing, grayscale, noise addition, median filtering, binarization, and morphological operations. Classification assesses benign or malignant melanomas, measuring accuracy.
Abstract
This study focuses on the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the segmentation and classification of melanoma skin cancer. The proposed methodology involves several key steps. Firstly, pre-processing techniques such as image resizing, converting to grayscale (rgb2gray), introducing noise (imnoise), median filtering (medfilt2), and binarization (imbinarize) are employed to enhance the quality and prepare the images for analysis. Subsequently, a process of morphological operations (bwmorph) aids in further refining the image segmentation. The classification stage distinguishes between benign and malignant melanomas. The deep learning model, particularly CNN, plays an important role in extracting complex features for accurate classification. The study evaluates the performance of the proposed approach by measuring classification accuracy, providing insights into the efficacy of utilizing deep learning for melanoma skin cancer diagnosis, which is crucial for early detection and timely medical intervention.
Keywords: Melanoma Skin Cancer Dataset, deep learning, image processing, Segmentation, Convolutional neural network and Accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
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
Disk:
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
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & 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:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
