Kidney Stone Images Classification

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Kidney Stone Images Classification :red_circle: Aim : The aim of this project is to classify the images given in the dataset using deep learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/safurahajiheidari/kidney-stone-images :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.

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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.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

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

[Jaya-Prakash-17]

Full name : Jaya Prakash Sangem (Im new to Open Source programs and also my GitHub profile is pretty much plain but, I have worked on similar project on classification of lung cancer from CT Scans, So, I hope that experice would help me here addressing this particular issue.)

GitHub Profile Link : https://github.com/Jaya-Prakash-17 Email ID : [email protected]; [email protected] Approach for this Project : -->Data Collection -->Data Preprocessing -->EDA (applying different filters like Guassian filter, gray scale etc to understand the image) --> Model Building & training (I paticularly want to try out various ML/DL Models, CNNs like(Resnet, Efficientnet, InceptionNet etc and check which model gives best results) What is your participant role?: Contributor @GSSOC24

So, kindly assign this issue to me. THANK YOU!!

[Kashish-G]

Hi, @abhisheks008 I m really excited to contribute to this project. Could you please assign it to me? I have experience in image segmentation using cellpose, Unet so I believe these expertise will help me here Full Name: Kashish Gandhi GitHub Profile Link: https://github.com/Kashish-G Email ID: [email protected] Approach for this Project:

1. Data Preparation: Collect kidney stone images with bounding box annotations. Preprocess and augment the data for training.
2. Model Selection: Choose a deep learning model like Faster R-CNN, YOLO, or SSD for object detection.
3. Training: Fine-tune the chosen model on the dataset, optimizing for accurate bounding box predictions using transfer learning.
4. Evaluation: Assess the model's performance using metrics like IoU or mAP on a validation dataset.
5. Post-processing: Apply NMS and refine bounding box predictions if necessary.
6. Continuous Improvement: Iterate on the model based on feedback and new data to enhance performance over time.

I am excited about the opportunity to contribute to this project and believe that my skills and enthusiasm align well with the goals of the initiative. I am committed to delivering high-quality work and collaborating effectively with the team. Participant of Gssoc'24

[abhisheks008]

Issue assigned to you @Kashish-G

[Kashish-G]

Issue assigned to you @Kashish-G

Thank you!