Lung Segmentation Model

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Lung Segmentation Model :red_circle: Aim : This project will help us to segment the lungs and identify accordingly. :red_circle: Dataset : https://www.kaggle.com/datasets/farhanhaikhan/unet-lung-segmentation-weights-for-chest-x-rays :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program and JWOC '22 Open Source Program.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.
• This issue is only for 'SWOC' and 'JWOC' contributors of 'ML-Crate' project.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID :
• Approach for this Project :
• What is your participant role?
  • [ ] SWOC 2.0 Participant.
  • [ ] JWOC 2022 Participant.
  • [ ] Contributor

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[TheDarkParalda]

Full name : Evan Joshy Chittilappilly GitHub Profile Link : https://github.com/TheDarkParalda Participant ID : ? Approach for this Project : CNN Models : VGGNET, ImageNET, ResNet and InceptionNet

[abhisheks008]

Issue assigned to you but please complete the previous one. @TheDarkParalda

[TheDarkParalda]

Unassign me in this. Sorry.

[Dyuksha27]

Full name: Dyuksha Singla GitHub profile link: https://github.com/Dyuksha27 Participation ID: NA Approach for this project: First I'll be analysing and then extract the data and after that I'll look for any missing values in the data if any. Finally, I'll create and train the model.

[Dyuksha27]

Hi, The link mentioned under the dataset section isn't working. If possible can you provide me with the correct dataset link if this issue is assigned to me.

[abhisheks008]

Updated the dataset. What are the models you are planning to use here? And in which open source event are you in? @Dyuksha27

[Dyuksha27]

Thank you for updating the dataset. I'm planning to use logistic regression, kNN as of now. As I'm new to this so I would like to explore more while working on it and will try to implement the same to make it better. Participant role: JWOC 2024

[abhisheks008]

You have to use deep learning models as this dataset contains X-ray images, normal machine learning models can't handle the noise. Can you do that? And you need to implement at least 3-4 deep learning methods for this project tbh!

[Dyuksha27]

I'll try my best and will do it. If at any point I need your guidance I'll ask you.

[abhisheks008]

Cool, issue assigned to you @Dyuksha27

[Praneet0327]

Full name : Praneet Singh GitHub Profile Link : https://github.com/Praneet0327 LinkedIn Profile : https://www.linkedin.com/in/praneet-s-1279b3225/ Participant ID : N.A. Approach for this Project : I will primarily work with the U-Net architecture due to its effectiveness in medical image segmentation and will study and explore newer deep learning architectures along the way. The trained model will be saved and deployed using Streamlit or Flask for practical use, with plans for continuous improvement based on new data and feedback. Participation role : SSOC'24 Contributor

Requesting you to assign this issue to me.

[abhisheks008]

Full name : Praneet Singh GitHub Profile Link : https://github.com/Praneet0327 LinkedIn Profile : https://www.linkedin.com/in/praneet-s-1279b3225/ Participant ID : N.A. Approach for this Project : I will primarily work with the U-Net architecture due to its effectiveness in medical image segmentation and will study and explore newer deep learning architectures along the way. The trained model will be saved and deployed using Streamlit or Flask for practical use, with plans for continuous improvement based on new data and feedback. Participation role : SSOC'24 Contributor

Requesting you to assign this issue to me.

Need to implement 3-4 models for this project. Find out the best fitted one based on the accuracy scores of the models. Then implement the best fitted model for the web app.

Assigned to you @Praneet0327

[Praneet0327]

Sure thing! Thanks

[Praneet0327]

Kindly unassign me. I won't be able to contribute further due to some personal commitments. Sorry for the inconvenience.