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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). [ABSTRACT FROM AUTHOR]

The patient was a 69-year-old right-handed woman. She had sensory aphasia, and the brain MRI revealed a subacute phase hemorrhage in the left subcortical temporal lobe. We speculated that the patient had post-ictal aphasia due to symptomatic epileptic seizures associated with cerebral hemorrhage. Seven months later, she was readmitted to the hospital with sensory aphasia; however, this time, the brain MRI FLAIR demonstrated high signal in the left medial temporal lobe and thalamic pillow. Furthermore, the electroencephalogram showed periodic discharges focused on the left temporal region. 123 I-IMP SPECT of cerebral blood flow indicated a cluster within the medial left temporal lobe. Aphasia associated with nonconvulsive seizures was considered. Sudden onset sensory aphasia should be differentiated from ictal and post-ictal aphasias.