• A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous system (CNS) tumors.

• The 5-year survival rate for people with cancerous brain or CNS tumor is approximately 34% for men and 36% for women.

• Brain tumors are classified as: Benign tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectanct of patients.

• The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by radiologists. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties.

• A brain tumor is considered one of the most aggressive diseases, among children and adults. With manual examination, it can be error-prone due to the level of complexity. Hence, adding Machine Learning (ML) and Artificial intelligence (AI) has consistently shown higher accuracy than manual classification.

Time and Cost Reduction in Healthcare: facilitates early detection of brain tumors and diagnosis, helps reduce patients' waiting time and save doctors from burn-out.

Assist doctors in treatment planning: produces sencond opinion in a time-ly manner can alert the doctors during the treatment planning process and speed uo workflow efficiency

Better performance Overall: When analyzing MRI, there must be a radiologist and a neurosurgeon on-site, hence, an automated system on Cloud can add a valuable second opinion in a timely manner

• Dataset: Brain Tumor Classification MRI and MRI Image Data

• Description: These 2 datasets contain over a total of 10,000 brain MRI images which are classified into 4 classes: no tumor, glioma, meningioma, and pituitary tumor.

This project aims to create and fine-tune different Convolutional Neural Network (CNN) using PyTorch, to find out which one performs the best.

• Make sure you have PyTorch and necessary libraries installed.

• Follow the steps outlined in the notebook, from data loading to model evaluation. After the first few tries, feel free to experiment different parameters.

1. Data Loading and Preprocessing: During image processing, we'll resize and shuffle the dataset before training. Besides, we'll also experiment with data augmentation to strengthen the performance.
2. CNN model Architecture:
3. Training: Splitting training and testing dataset at 9:1 ratio and run through different number of epochs to test the efficiency.
4. Evaluation Evaluate each model on the validation set.

Python

Pandas

DifPy

Tensorflow

Scikit-learn

PyTorch

Resnet34

In this project, we experience 3 different models with increasing complexities to see how the accuracy is improved over time:

• 1 layer of CNN (most basic)
• 9 layers of CNN
• Resnet34: a CNN architectures that is pre-trained on ImageNet Dataset containing 100,000+ images accross 200 different classes

The table below will display various accuracy achieved by diffrent models:

• The future website/app is intended to classify MRI brain tumor into 4 clasess: no tumor, glioma, meningioma and pituitary.
• This website/app should be used as an add-on and shouldn't be considered as professional diagnosis. The main advantage is to give a second opinion in a timely manner

To further enhance the performance and capabilities as well as practicality of the CNN model, the following steps are recommended:

1. Model refinement

• Data input: The more data coming in, the better the model will be trained and learn different patterns of MRI. Currently, the model is being trained on a limited dataset, hence, the performance will somehow be limited
• Hyperparameter tuning: higher epochs don't necessary guarantee better performance, looking into playing with batch sizes, learning rates, etc. might give better performance

2. Implement Transfer Learning:

• In this project, we touch base of Transfer Learning with Resnet34. Feel free to experience with other Resnet architectures such as Resnet18, Resnet50, etc.