Paper Publishing3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images
Objective
Detection of Prostate Cancer Gland using YOLO network (Deep Learning Technique) is performed.
Abstract
In this work, detection of Prostate Gland is performed using 3D Magnetic Resonance Images (MRI). Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer.
Although many automated detection approaches including those based on image processing techniques have been proposed, the detection performance still has room for improvement due to the large variability in image appearance, imaging interference. Here, we will implement the detection of prostate gland using deep learning technique like YOLO network.
The dataset for this work is collected from the database of Cancer Imaging Archive. We evaluated the proposed network against several state-of-the-art deep learning-based segmentation approaches on CIA databases.
Keywords: Prostate Gland, Detection, Magnetic Resonance Images (MRI), YOLO network.
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:
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RAM:
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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:
- Acquisition
- Image enhancement:
- Image restoration:
- Color image processing:
- Image compression:
- Morphological processing:
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will able to know, what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to diagnosis the disease using AI
- How to extend our work to another real time applications
- Project development Skills
- Problem analyzing skills
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- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
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