[Project Addition] : Brain Tumor Detection

[aaradhyasinghgaur]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Brain Tumor Detection

🔴 Aim : Accurately detecting and classifying brain tumors is crucial yet challenging. Deep learning, particularly convolutional neural networks (CNNs), can automate this process by analyzing MRI scans, reducing the time and variability associated with traditional methods.

🔴 Dataset : https://www.kaggle.com/datasets/denizkavi1/brain-tumor

🔴 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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🔴🟡 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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✅ To be Mentioned while taking the issue :

• Full name : Aaradhya Singh
• GitHub Profile Link : https://github.com/kyra-09
• Email ID : [email protected]
• Participant ID (if applicable):
• Approach for this Project :

1. Utilizing Multiple Network Architectures:
   To achieve categorical classification of brain MRI images for detecting different types of brain tumors, we will leverage five distinct deep learning network architectures:

   • DenseNet121
   • Xception
   • VGG16
   • ResNet50
   • InceptionV3

2. Data Augmentation Techniques:

To enhance the accuracy and robustness of the models, we will apply various data augmentation techniques such as:

• Rotation
• Zooming
• Flipping (horizontal and vertical)
• Shearing
  Brightness adjustments

These techniques will artificially expand the dataset and help prevent overfitting.
3. Model Performance Comparison:

I will evaluate and compare the performance of each model using the following metrics and visualizations:

- Accuracy Score: To measure the overall correctness of the models.
- Loss Graph: To visualize the loss during training and validation phases.
- Accuracy Graph: To track accuracy improvements over epochs.
- Confusion Matrix: To provide a detailed breakdown of model performance across different tumor types, highlighting precision, recall, and F1 score for each category.

4. Exploratory Data Analysis (EDA):

Before training the models, we will perform comprehensive exploratory data analysis (EDA) on the dataset to understand its structure. This will include:

- Distribution of images across different tumor types.
- Image quality and resolution consistency.
- Identifying any class imbalances.
- Visualizing sample images from each category.

5. README File:

A README file will be created to document the entire process .

• What is your participant role? (Mention the Open Source program) GSSOC-2024 contributor

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Happy Contributing 🚀

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

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊