Experimental Analysis of Brain Tumors Identification Methodology Using Artificial Intelligence (AI) Powered Learning Technique

Brain tumors are defined as an abnormal proliferation of tissue inside the cerebral cortex. In addition to exerting pressure on the brain's healthful regions, it can result in substantial health complications. A variety of health problems may result from a brain tumour, contingent upon its location. Malignant brain tumours expand swiftly, which can result in a significant increase in the mortality rate of those diagnosed with this cancer with each passing week. Therefore, it is imperative to identify these tumours at an early stage in order to implement preventive measures during the initial phases. AI, in conjunction with recent technologies such as Deep Learning, plays a critical role in the early detection of this disorder. This paper introduces a novel approach; Convolutional Artificial Intelligence assisted Classification (CAIC), which is used to identify brain tumours using Computed Tomography (CT) images. The proposed model is cross-validated with the conventional learning approach, Support Vector Machine (SVM), to assess its efficiency. By reducing the size of the lesion and its connections to brain regions, AI-driven techniques are already making a big impact in surgical planning. This makes it possible to have precision brain surgery that is as extreme as is ethically permissible while yet preserving life quality. In conclusion, the proposed AI-assisted model CAIC can anticipate problems, recurrences, and treatment response; hence, it can advise on the most suitable follow-up. Biochemical and clinical data will likely be integrated by AI-powered algorithms in the future to improve risk stratification and patient individualized screening procedures