A Deep Learning Approach for Brain Tumor Classification

Abstract

The medical field is incredibly stressful for both specialists and patients, especially when dealing with life threatening cases. Brain tumor identification is one of the most challenging tasks even for experts in the field. Fortunately, the evolution of machine learning techniques and their flexibility to be integrated into many different fields, including tumor identification and classification has greatly simplified this task. For the physicians to accurately diagnose a patient, they must first have a lot of knowledge and years of experience in addition to going through many cases. Machine learning simplifies this process when it comes to learning and using this knowledge to decide without human intervention. If the data fed to the model is accurate, the decisions made will be more accurate. In this paper, an automated machine learning model is developed to identify brain tumors. Different datasets are utilized in the developed model. The accuracy achieved by the model lies between 60% and 70%. Another dataset is then used, and the model shows an accuracy of 99%, precision of 83%, F1 score of 77% and recall of 75%.