Ensemble Technique for Brain Tumor Patient Survival Prediction.

Brain Tumours pose a significant health challenge, demanding the immediate development of reliable and automated detection methods within the medical sphere. Swift and accurate identification of these Tumours are crucial for effective treatment and the well-being of patients. These growths stem from uncontrolled cell multiplication, depleting vital nutrients from healthy brain tissue and leading to organ dysfunction. Presently, the conventional method involves a manual examination of brain MRI scans by medical professionals, but this is hindered by the varied shapes and sizes of Tumours, resulting in time-consuming and occasionally imprecise evaluations. The emergence of automation holds immense potential, promising to bolster efficiency and allow medical practitioners more time for direct patient care. Traditional machine learning approaches have historically depended on labor-intensive feature engineering. In our research, we introduce an innovative approach: a combination of the U-Net model, a Convolutional Neural Network (CNN), and Self Organizing Feature Map (SOFM) in an ensemble technique for precise brain Tumour segmentation using the BRATS 2020 dataset. Our evaluation not only focuses on segmentation accuracy but also utilizes valuable survival data from the dataset to predict patient survival rates. The proposed model resulted in average training accuracy, mean Intersection over Union (mIoU), and dice coefficient scores of 0.967, 0.521, and 0.990 respectively for different epochs. Also, average validation accuracy, mIoU, and dice coefficient scores of 0.965, 0.546, and 0.992 respectively. The proposed model showcases a 98.28% accuracy in the segmentation of brain Tumours. The proposed methodology has the potential to revolutionize the landscape of brain Tumour diagnosis and treatment.