An Ensemble Approach for Comprehensive Brain Tumour Detection Using MRI-Based Machine Learning Models - Asad Shaikh, John Baby, Shivam Goswami, Kavita Jain, Deepali R. Vora,Teena Varma, Harshali Patil, Adit Anil Deshmukh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the realm of medical imaging, the rapid evolution of techniques and exponential growth of data have emphasised the significance of automatic and reliable tools for brain tumour detection. This project proposes a system designed to detect brain tumours utilising Magnetic Resonance Imaging (MRI) data. Two distinct models leveraging advanced machine learning algorithms, particularly Convolutional Neural Networks (CNNs), are developed using multiple datasets. The BraTS dataset is used for segmentation tasks, providing detailed information about specific brain tumour regions. Concurrently, the Br35H dataset is used for binary classification, distinguishing the presence or absence of tumours. Furthermore, the brain tumour dataset from Kaggle adds another dimension to this study, offering diverse data samples. The proposed system encompasses a two-step approach. First, a segmentation model is fine-tuned on the BraTS dataset to identify specific regions within brain scans. Subsequently, a classification model is trained using both the Br35H and Kaggle datasets. Ensemble learning techniques, involving an ensemble of CNN architectures such as ResNet and VGG, along with the exploration of ensemble methods like AdaBoost, are employed for effective classification.