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Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.

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Medical-Image-Segmentation-DL

Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.


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Medical Image Segmentation to Detect And Classify Colorectal Polyps

Table of Contents
  • Business Objective
  • Goal
  • Tech Stack
  • Data and Code Overview
  • Approach Steps
  • Project Takeaways
  • Business Objective

    Machine learning and deep learning technologies are increasing at a fast pace with respect to the domain of healthcare and medical sciences. These technologies sometimes even out perform medical doctors by producing results that might not be easily notable to a human eye. Polyp recognition and segmentation is one such technology which helps doctors identify polyps from colonoscopic images

    Goal

    To segment the polyps from colonoscopy images

    The pdf file Solution_Methodology.pdf has the details for the complete methodology used in the project.

    Star⭐ the repo if you like what you see😉.

    Tech Stack

    Deep learning library used : Pytorch

    Computer vision library used : OpenCV

    Other python libraries :

    Data and Code Overview :

    CVC-Clinic database consists of frames extracted from colonoscopy videos. The dataset contains several examples of polyp frames & corresponding ground truth for them.The Ground Truth image consists of a mask corresponding to the region covered by the polyp in the image. The data is available in both .png and .tiff formats

    Data Src : https://www.kaggle.com/balraj98/cvcclinicdb

    This is the code overview setup of the project

    Code overview Screen Shot

    Approach Steps

    1. Data Understanding : Understanding the essence of the dataset

    2. Understanding evaluation metrics:Understanding the metrics that are going to be used for evaluating the predictions

    3. Unet Architecture :Understanding Unet architecture and why is it preferred widely in building deep learning models with respect to medical sciences.

    4. Unet ++ :Understanding Unet++ and how is it different from Unet

    5. Environment Setup : Setting up a working environment for the project

    6. Data Augmentation : Creating new data by making modifications on the existing data

    7. Model building : Building Unet ++ model using pytorch

    8. Model Training; Training the model. ( A GPU might be required since model training takes a really long time in CPUs)

    9. Model Prediction

    Project Takeaways

    1. Understanding Polyp Segmentation Problem

    2. Understanding IOU

    3. Understanding Data augmentation

    4. Data augmentation using pytorch

    5. Understanding Computer vision and its applications in medical field

    6. Understanding and implementing CNN models

    7. OpenCV for computer vision

    8. Understanding VGG,Unet and Unet++ architectures

    9. Building VGG block using Pytorch

    10. Building Unet++ network using Pytorch

    11.Training and predicting Unet++ models

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    Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.

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