Comparative Analysis of Deep Learning and Machine Learning for Detection and Classification of Brain Tumors at Multiple Stages

The brain acts as the body's command central location. As time goes on, more and more brain illnesses are pinpointed. Because of this diversity, improving current methods of diagnosing or detecting brain illnesses is a constant focus of study. The effectiveness of treatment for disorders of the brain can be greatly improved by catching them early. Recent years have seen the widespread adoption of AI across the scientific community, and without a doubt, this has had a profound impact on neurology. Brain disease diagnosis and prognosis have both benefited from AI's incorporation into the medical industry. Cancer of the brain is a major killer in the world today. Because of these characteristics, there are many ways to check for brain cancer. Prompt diagnosis is essential for effective treatment of brain tumors. One such technique is magnetic resonance imaging. In contrast, state-of-the-art approaches have been employed to address several classification-related challenges in medical imaging in recent years. These include deep learning, neural networks, and machine learning. To differentiate between glioma, meningioma, pituitary, and no tumour in patients with brain cancer, the SVM classifier in machine learning (ML) was utilized in this study. The information in this study comes from people's contrast-enhanced MRI images. This study provides a comparison between the suggested model and competing models to prove the superiority of our method. Both the raw data and the data after it had been cleaned and supplemented were evaluated. In this research, we explore the possibility of using deep learning and other advanced machine learning approaches to spot brain abnormalities. To be noteworthy concerns with machine learning/deep learning-based methodologies for identifying brain illnesses are discussed, and the most noteworthy findings from the publications we analyzed are presented. The ultimate purpose of the research is to determine the best method for diagnosing various forms of brain diseases.