Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review


  • Noor Mohammed Ghadi College of Sciences - University of Baghdad
  • Nassir H. Salman College of Sciences - University of Baghdad



Brain Tumor, Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN), Classification, Segmentation, Feature Extraction.


Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.


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How to Cite

Ghadi, N. M. and Salman , N. H. . (2022) “Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review”, Journal of Engineering, 28(12), pp. 93–112. doi: 10.31026/j.eng.2022.12.07.