Deep Convolutional Neural Network for Spider Vein Detection
Objective
The objective of this project is to develop a deep convolutional neural network (DCNN) for spider vein detection in medical images. The DCNN will be trained on a dataset of images that includes both positive and negative examples of spider veins, and will be optimized to accurately detect and classify spider veins in images
Abstract
Varicose vain is a chronic disease which occurs when leg vein blood circulation is not working properly. This cause problem in the blood circulation from leg to heart. This occurs because of blood get collected in the leg veins and this condition is said to as stasis which leads blood to drop backward and damage the valve. Prolong standing or sitting, aging, lack of mobility are some of the main reason for this chronic disease. The cost for treatment is also too high. Early detection of this problem can be treated easily and help the patient relief from pain and stress. Deep learning technique plays a major role in early prediction and to identify the presence of varicose vein and assist the clinician. Here the proposed model developed through convolutional neural network outperforms doctor’s diagnosis and provide better accuracy in classifying the image as Normal or Varicose through which the patient can be treated appropriately.
Keywords: Varicose Vein, Convolutional Neural Network (CNN), Chronic Disease.
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 2018a 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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+91 9393939065
0877-2261612
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.
