Brain Tumor Detection

[aaradhyasinghgaur]

Is your feature request related to a problem? Please describe. 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. By training on large datasets, CNNs can provide consistent, accurate, and efficient tumor detection and classification, significantly aiding radiologists in diagnosis and treatment planning.

Describe the solution you'd like

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 .

Dataset I'll use :- https://www.kaggle.com/datasets/denizkavi1/brain-tumor

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.

@TAHIR0110 Kindly assign this issue to me and review my existing pr so that I can start working on issues.

[TAHIR0110]

@kyra-09 the major issue with the detection from images is when we use open cv to take the MRI scans using our camera rather than taking image input is that it only detects at a certain angle, I'm labelling this issue as level3 and would expect you to make a good model which would take image from the camera and would detect not perfectly but almost accurately with most of the angles considering lightning is being taken care of.

[aaradhyasinghgaur]

I'm not using openCV the tools I'll be using Keras API , scikit-learn , numpy , matplotlib for visualisation , tqdm etc. but I get your point...in case of any irregularities I'll make sure the models work accurately though I'll add custom layers and apply data augmentation techniques such as shearing, rotation etc. and will make pr ASAP. Could you also look into my existing pull requests and in case of no problems could you merge them into the repo?

[Idk507]

Hi @kyra-09 Will you please assign me this issue ,I would like to work on it.