Title: Brain Tumor Detection

[aaradhyasinghgaur]

Is your proposal related to a problem? Please describe.

Description :- 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.

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

Solution I propose :-

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 .

I'll also add extra custom layesr to enhace the performnace of the models. I want to add his projetc to advance ml/datascience repo.

Tools I'll use ;- keras , numpy , matplotlib , sckit-learn , tqdm etc.

Kindly assign this issue to me @Kushal997-das and lable it as level-3 if possible as I'm training and testing dataset for 5 different models which is resource and time consuming and also making doucumentation about which works better . I will also be doing EDA analysis for the dataset.

Add any other context or screenshots about the proposal request here.

No response

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[aaradhyasinghgaur]

@Kushal997-das kindly assign this issue to me please.