Predicting Brain Tumor Type from MRI Images Using Ensemble of Deep Learning Features and Random Forest

The goal of this project was to develop an accurate classification system to predict the type of brain tumor based on MRI images. To achieve this, a combination of pre-trained deep learning models, including VGG16, ResNet, MobileNet, and DenseNet, was utilized to extract meaningful features from the MRI images. The extracted features were then combined using an ensemble technique.

The ensemble approach aims to leverage the strengths of multiple deep learning models to enhance the predictive performance. By combining the features extracted from different architectures, the ensemble method aims to capture a broader range of relevant information for accurate tumor classification.

To perform the final classification, two popular machine learning algorithms, namely Random Forest and XGBoost, were employed. Random Forest, in particular, demonstrated superior performance with an accuracy of 94%. This model was selected as the best-performing algorithm for predicting the tumor class.

The dataset used in this project consisted of MRI images from patients diagnosed with meningioma, glioma, pituitary tumors, or no tumor. The dataset was appropriately preprocessed and split into training and testing sets to ensure unbiased evaluation of the developed models.

Throughout the project, significant attention was given to data quality and model evaluation. To mitigate overfitting, appropriate regularization techniques, such as dropout and weight decay, were employed during the training process of the deep learning models. Moreover, hyperparameter tuning was performed to optimize the performance of both the deep learning models and the machine learning algorithms.

The achieved accuracy of 94% on the test set demonstrates the effectiveness of the proposed approach. This high accuracy suggests that the ensemble of deep learning features combined with the Random Forest classifier can accurately identify and classify different types of brain tumors based on MRI images.

This project has practical implications in the medical field as an efficient tool for assisting healthcare professionals in diagnosing brain tumors. With further validation and refinement, this system could potentially be integrated into clinical workflows to aid radiologists and oncologists in making accurate and timely diagnoses, ultimately improving patient outcomes.

In conclusion, this project successfully developed a robust classification system for brain tumor identification using an ensemble of deep learning features extracted from MRI images and the Random Forest algorithm. The achieved accuracy of 94% showcases the potential of this approach for assisting medical professionals in the diagnosis of brain tumors.