AI-Powered Prostate Cancer Detection using image processing & deep learning
Objective
To develop an AI-powered prostate cancer detection system that utilizes advanced image processing and deep learning to enhance diagnostic accuracy, facilitate early detection, and improve treatment planning for better patient outcomes.
Abstract
The proposed AI-powered prostate cancer detection system leverages advanced image processing and deep learning techniques to enhance the accuracy of early diagnosis and treatment planning. The process begins with preprocessing the input images by removing noise and restoring image quality, ensuring a clear and accurate dataset for analysis. A Convolutional Neural Network (CNN) is then employed to classify the images into two categories: normal or prostate cancer. For images identified as showing signs of prostate cancer, the system applies a K-means clustering-based segmentation technique to further analyze the affected regions. This segmentation allows the system to accurately determine the cancer stage, which is crucial for recommending the most appropriate treatment options. By integrating image processing with deep learning, this approach not only improves diagnostic precision but also assists in the early detection and effective management of prostate cancer, potentially leading to better patient outcomes.
Keywords: Prostate Cancer MRI Dataset, Pre-Processing, Convolutional Neural Networks, Deep learning, Classification, 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
Demo Video
Request for Video
Paper PublishingRequest 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.
