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Brain Tumor Detection Using Machine Learning and Deep Learning: A Review


Venkatesh S. Lotlikar*, Nitin Satpute and Aditya Gupta  


According to the international agency for research on cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as magnetic resonance imaging (MRI) and computed tomography scans using a computer-aided design. Machine learning and deep learning techniques have gained significance among researchers in medical fields, especially convolutional neural networks (CNN), due to their ability to analyze large amounts of complex image data and perform classification. The objective of this review article is to present an exhaustive study of techniques such as preprocessing, machine learning, and deep learning that have been adopted in the last 15 years and based on it to present a detailed comparative analysis. The challenges encountered by researchers in the past for tumor detection have been discussed along with the future scopes that can be taken by the researchers as the future work. Clinical challenges that are encountered have also been discussed, which are missing in existing review articles.


Brain tumor, Magnetic resonance imaging, Preprocessing, Machine learning, Deep learning, Convolutional neural networks


MTech scholar, Department of E&TC Engineering, College of Engineering Pune, Electrical and Computer Engineering, Aarhus University, Adjunct Faculty, Department of E&TC Engineering, College of Engineering Pune

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