Breast Cancer Recognizable Proof, Classification and Discovery Utilizing Neural Networks
Objective
The primary objective of the "Breast Cancer Recognizable Proof, Classification, and Discovery Utilizing Neural Networks" project is to develop a robust and efficient system for the early detection, classification, and exploration of breast cancer utilizing advanced neural network techniques.
Abstract
Artificial neural networks are widely used tools in various fields of medicine and technology. Breast cancer is the world’s second biggest cause of death for women. One of the key issues in medicine is determining the right diagnosis of breast cancer.
More effective procedures for early and precise diagnosis are constantly being developed. More than 10% of women develop breast cancer after lung cancer. Breast tumor can be identified by the following features: Redness in skin, size change, pain in abdominal, etc. Changes in the texture. The main causes of breast cancer sometimes it is very vague, but the disease is difficult to stop. Neural networks have become a very popular tool in diagnosing breast cancer and classifying cancer datasets.
Artificial neural networks are a field of artificial intelligence and are widely used as new technologies in the field of computer science. We have used neural networks to find out the tumor is Benign or Malignant. The input are just a bunch of measurement and features of tumor. Some of these parameters include measurement of the nuclei of cells in the tumors.
Keywords: Cancer, Breast Cancer, Neural Networks, classification.
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
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.
