Breast Cancer Classification using Capsule Network with Pre-processed Histology Images
Objective
To classify breast cancer tissue samples into Benign, In situ, Invasive, and Normal, this study uses Capsule Networks with pre-processed histology images, aiming to improve diagnostic accuracy and interpretability.
Abstract
In this research study, we propose a novel approach for the classification of breast cancer using a Capsule Network, a specialized deep learning architecture, with preprocessed histology images. The primary objective is to accurately categorize breast tissue samples into four distinct classes: Benign, In situ, Invasive, and Normal. Our method leverages the power of deep learning to automatically extract complex features from the histology images, enabling robust discrimination among these diverse tissue types. By employing Capsule Networks, which excel in modeling hierarchical patterns, we aim to enhance the interpretability of the classification process and improve overall accuracy. This innovative approach holds great promise for improving the efficiency and reliability of breast cancer diagnosis, ultimately contributing to more effective patient care and treatment planning.
Keywords: Breast Cancer Dataset, Pre-processing, Capsule 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.
