Deep Learning Based Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images
Objective
The main objective of this project is to predict the cervical cancer as either a positive or negative using Convolution neural Network (CNN) based on the transfer learning of the deep learning.
Abstract
Cervical cancer causes the fourth most cancer related deaths of women worldwide. Early detection of cervical intraepithelial neoplasia (CIN) can significantly increase the survival rate of patients. In this paper, we propose a deep learning framework for the accurate identification of the cervical cancer is either positive or negative. Here we are using the Convolution Neural Network (CNN) based transfer learning which is a RsNet50 of the deep learning. The cnn is used to train the dataset of the cervical cancer which we have collected some of the images from the internet source and some are been augmented. CNN performs accurately and predicts the disease either as positive or negative.
Keywords: Cervical cancer, Deep Learning, CNN, Transfer Learning, Resnet50
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 8GB (min)
- Hard Disk: 128 GB
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Flask, Numpy, OS.
Learning Outcomes
- Testing techniques
- Error correction mechanisms
- What type of technology versions is used?
- Working of Tensor Flow
- Implementation of Deep Learning techniques
- Working of CNN algorithm
- Working on transfer learning
- Working of ResNet50 algorithm
- Building of model creations
- Scope of project
- Applications of the project
- About Python language
- About Deep Learning Frameworks
- Use of Data Science
- Practical exposure to
- Hardware and software tools
- Solution providing for real-time problems
- Working with team/individual
- Work on creative ideas
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