Detecting tumor severity using machine learning (ML) and deep learning (DL) techniques in MRI scans has become an increasingly promising area in medical research. Here’s a high-level overview of the process:

Data Collection and Preprocessing:

• Gathering a dataset of MRI images with labeled tumor severity levels.
• Preprocessing involves normalization, resizing, and noise reduction to standardize the images and enhance their quality.

Feature Extraction:

• Extracting meaningful features from the MRI images.
• For instance, in DL, this could involve using convolutional neural networks (CNNs) to automatically learn relevant features.

Model Development:

• Utilizing ML/DL models to classify tumor severity levels based on the extracted features.
• ML algorithms Random Forests is employed, along with DL models like CNNs-based ResNet-50 for detection tasks.

Training and Validation:

• Splitting the dataset into training, validation, and testing sets.
• Training the model on the training set and validating its performance on the validation set to fine-tune hyperparameters and avoid overfitting.

Dataset Link: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/data

Open in Kaggle for best peformance:

1. Click on the link
2. Click on New Notebook
3. Upload the code
4. Run all and enjoy!