Automating the Analysis Detection and Classification of the Brain Disease Using A Deep Learning Algorithms

Abstract

Beyond all expectations, computers have an astounding range. Other than for storage, computers are used in practically every industry, including finance, entertainment, e-commerce, transportation, healthcare, and many more. On the other hand, innovations in big data, artificial intelligence, data mining, and machine learning are influencing how future research in a variety of subjects will be conducted. Information extraction from data is difficult at the moment. The results of the various conclusions of data mining techniques have also made decision-making easier. The current research direction, particularly in computer science, is to make predictions about future outcomes in a particular field. Building a model that can make predictions is the general idea behind predictive modelling. A machine learning technique is typically used in these models to learn attributes from training datasets and predict outcomes. The patient must endure a number of costly and time-consuming tests in order to diagnose any illness or aneurysms. And occasionally, these might not work. Numerous pieces of research have been carried out to cut down on the number of diagnostic tests. The performance of prediction approaches using machine learning-based tactics has proven promising and is currently the most popular method. When medical professionals are still in the beginning stages of their employment, they play a critical role in preventing incorrect diagnoses. Additionally, it is vital to note that these techniques are crucial since they aid in early aneurysm prediction. There is a lot of research being done on the use of machine learning techniques actively in the medicinal field. It primarily focuses on simulating some human behaviours or thought processes and identifying diseases using a number of input sources. Recent technological and computer improvements have made it easier to regularly gather and store medical data that can be utilized to assist treatment choices. However, gathering and organizing patient data in digital form