Classification of brain tumor using deep learning at early stage

Abstract

Early detection and classification of brain tumors are crucial for patient survival. This study proposes a comprehensive deep learning approach for early brain tumor classification using medical imaging data. A diverse dataset encompassing various tumor types, stages, and healthy brain images is utilized. Preprocessing techniques like augmentation and normalization enhance data robustness. A convolutional neural network (CNN) architecture serves as the primary model, leveraging transfer learning from pre-trained models to extract relevant features even with limited data. The training process optimizes hyperparameters to prevent overfitting, and performance is evaluated using metrics like accuracy, precision, recall, F1 score, confusion matrices, and ROC curves on a separate test set. Focusing on early detection, the model explores predicting tumor growth trajectories and identifying subtle pre-tumor patterns, aligning with expert diagnoses and boosting real-world applicability. Ethical and regulatory guidelines are adhered to in data handling. Continuous improvement involves updating the model with new data and monitoring its clinical performance. This research contributes to advancing early tumor classification methods, potentially improving patient outcomes and treatment strategies.

Keywords

• Mask R–CNN (region-based convolutional neural networks)
• Support vector machines (SVM)
• Intersection over union (IoU)
• MRI (magnetic resonance imaging)