ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
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.
Solution :-
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
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.
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.
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.
README File:
A README file will be created to document the entire process .
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.
Solution :-
Data Augmentation Techniques: To enhance the accuracy and robustness of the models, we will apply various data augmentation techniques such as:
These techniques will artificially expand the dataset and help prevent overfitting.
Confusion Matrix: To provide a detailed breakdown of model performance across different tumor types, highlighting precision, recall, and F1 score for each category.
Before training the models, we will perform comprehensive exploratory data analysis (EDA) on the dataset to understand its structure. This will include:
A README file will be created to document the entire process .
Dataset I'll use :- https://www.kaggle.com/datasets/denizkavi1/brain-tumor
@invigorzz313 kindly assign this issue to me.