Brain Tumor Detection using CNN: Achieved 96% accuracy in tumor classification utilizing TensorFlow for model development and optimization.😎🔐

Experience the power of deep learning with our Kaggle repository! We present a comprehensive implem$

1. Importing Essential Libraries: ✅

Start by importing necessary libraries for data manipulation, visualization, and model construction wi$

2. Setting Up Dataset Paths and Directories: ✅

Define the dataset path and training/testing directories, and establish the categories for brain tumor$

3. Loading and Preprocessing the Dataset: ✅

Read images from each category in the training directory, create a DataFrame to store image data, and $

4. Visualizing Images for Each Tumor Type: ✅

Display sample images for each tumor type in a grid of subplots.

5. Setting Image Size, Batch Size, and Epochs: ✅

Define image dimensions, batch size for training, and the number of training epochs.

6. Data Augmentation and Preprocessing: ✅

Augment the training dataset to improve model generalization using techniques like rotation, shifting,$

7. Building the Model Architecture: ✅

Construct a CNN model with convolutional, max pooling, dense, and dropout layers. Compile the model us$

8. Training and Validation: ✅

Train the model using the training generator, tracking accuracy and loss over epochs. Validate on the $

9. Visualization Through Graphs: ✅

Plot training and validation accuracy/loss curves over epochs to monitor the model's learning progress.

10. Evaluation: ✅

Evaluate the model's performance on the test dataset, calculating test loss and accuracy.

11. Confusion Matrix and Explanation: ✅

Visualize a confusion matrix to analyze the model's classification performance for each class. Display$

12. Precision, Recall, and F1-Score Calculation: ✅

Calculate precision, recall, and F1-score for each class from the confusion matrix.

13. Saving the Trained Model: ✅

Save the trained model to a file for future use or deployment.

Unlock the potential of CNNs for brain tumor detection through our meticulous implementation, demonstr$