Analysis of Hybrid Fusion-Neural Filter Approach to detect Brain Tumor

Abstract

Medical Image Processing plays an essential role in human health. Many methods have played an essential role in reducing physician decision-making in diagnosis. Much caution is required and recommended, especially in cases involving the brain. Separation of tumors from normal brain cells belongs to the category of brain tumors. The dissection process can help provide the information needed for diagnosis. This process is risky due to the unusual shapes and manipulations at the border. Determining these tumors at an early stage can help provide the best treatment for patients. Typically, physicians adopt a manual method of dividing patients into patients, which leads to more time. This paper presents a well-functioning Hybrid Fusion-Neural Filter Approach (HFNF)classification system that considers various factors such as accuracy, recovery and accuracy. MRI is one of the most traditional methods for the primary diagnostic tool for brain tumors. If the tumor is malignant for successful treatment, the necessary diagnostic and treatment planning measures must be taken quickly. Physicians can make accurate decisions by applying the following procedures. The necessary treatment can be done effectively. A computer-assisted diagnostic system, MRI, can help reduce the workload of physicians.