Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique

The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identification and classification of brain tumors and hemorrhages, which can significantly impact patient prognosis and treatment planning. The objectives of the study address the prediction of brain cancer occurrence and the assessment of risk levels associated with both brain cancers due to brain hemorrhage. A diverse dataset of brain MRI and CT scan images. Utilize Unsymmetrical Trimmed Median Filter with Optics Clustering for noise removal while preserving edges and details. The Chan-Vese segmentation process for refined segmentation. Brain cancer detection using Multi-Head Self-Attention Dilated Convolution Neural Network (MH-SA-DCNN) with Efficient Net Model. Brain cancer detection using MH-SA-DCNN with Efficient Net Model. This trains the algorithm to predict cancerous regions in brain images. Further, implement a Graph-Based Deep Neural Network Model (G-DNN) to capture spatial relationships and risk factors from brain images. Cox regression model to estimate cancer risk over time and fine-tune and optimize the model's parameters and features using the Osprey optimization algorithm (OPA).