Kidney Stone Detection with CT Images using Neural Network
Objective
The overall goal of this project is to provide a reliable, fast, and accurate tool for the early detection and diagnosis of kidney stones, which can lead to better patient outcomes and reduce healthcare costs.
Abstract
Back Propagation Network (BPN) with image and data processing techniques are employed to implement an automated kidney stone classification. By human inspection and operators, it is impossible to produce result for large amount of dataset. CT scan and MRI produces a lot of noise and hence leads to inaccuracies. Artificial intelligent techniques through neural networks techniques have shown great importance in this field. Hence, in this project we are applying the Back-Propagation Network (BPN) for the purposes. Features are extracted using GLCM and are then classified using BPN. This project presents a segmentation method, Fuzzy C Mean (FCM) clustering algorithm, for segmenting computed tomography images to detect the kidney stones in its early stages.
Keywords: Computed Tomography Image, CT Scan Images, Back Propagation Neural Network, BPN, Fuzzy Clustering Means, Fuzzy C-Means, Gray Level Co-occurrence Matrix.
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
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+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.
