ASBTRACT

• In this project, we propose an automatic 3D segmentation method for transrectal ultrasound breast images, which is based on multi-atlas registration and statistical texture prior.
• The atlas database includes registered breast images from previous patients and their segmented breast surfaces.
• Three orthogonal Gabor filter banks are used to extract texture features from each image in the database.
• Stage-specific Tumor features from the datasets are used to train (CNN-LSTM) and then to segment Using Super Pixel Segmentation the breast image from a new patient.

EXISTING SYSTEM

• Human based systems are involved in the breast Cancer Detection process.
• But this type of systems are having reliably issues with man made error chances.
• The most commonly used technique Auto encoder-Based Neural Network Algorithm is not a straightforward task due to the great variety of tumor lesions, low contrast between the lesion and the surrounding breast, irregular and fuzzy lesion borders breast types and presence of molecules.

DISADVANTAGES

• False detection.
• Cannot operate on images with less amount of contrast.

PROPOSED SYSTEM

• The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer.
• Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection.
• The scope of CNN_LSTM is used.
• Two phases are training and testing.
• Early detection of Cancer will reduces the complication.
• Reduction in significant amount of workload and time for ophthalmologists.
• CNN-LSTM is able to generalize since they are trained by example.
• This is the first real step towards the real development of a machine learning model, collecting data. This is a critical step that will cascade in how good the model will be, the more and better data that we get, the better our model will perform.
• There are several techniques to collect the data, like web scraping, manual interventions and etc.
• Breast Cancer dataset taken from kaggle and some other source.

PROJECT VIDEO

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements

• •Hard Disk: Greater than 500 GB
• •RAM: Greater than 4 GB
• •Processor: I3 and Above

Including Packages

=======================

• * Base Paper
• * Complete Source Code
• * Complete Documentation
• * Complete Presentation Slides
• * Flow Diagram
• * Database File
• * Screenshots
• * Execution Procedure
• * Readme File
• * Addons
• * Video Tutorials
• * Supporting Softwares

Specialization =======================

• * 24/7 Support * Ticketing System
• * Voice Conference
• * Video On Demand 
• * Remote Connectivity
• * Code Customization
• * Document Customization 
• * Live Chat Support

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