• 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.


  • 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.


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


  • 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.


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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