Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019
By Arti Tiwari, Shilpa Srivastava, Millie Pant. Pattern Recognition Letters, 2020
299 citations recorded by the source corpus.
Abstract The past few years have witnessed a significant increase in medical cases related to brain tumors, making it the 10th most common form of tumor affecting children and adults alike. However, it is also one of the most curable forms of tumors if detected well on time. Consequently scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumor. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) are two methods widely used for resectioning and examining the abnormalities in terms of shape, size or location of brain tissues which in turn help in detecting the tumors. MRI, due to its advantages over CT scan, discussed later in the paper, is preferred more by the doctors. The way towards sectioning tumor from MRI picture of a brain cerebrum is one of the profoundly engaged regions in the network of medical science as MRI is non-invasive imaging. This paper provides a systematic literature survey of techniques for brain tumor segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques, metaheuristic techniques and hybridization of these two. It includes presentation and quantitative investigation used in conventional segmentation and classification techniques.
Subfields: Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/1a6b7243455dff0db201f11d1cb64d3b1db4ebb4
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In Computer Vision and Pattern Recognition
In Neurology
Citations in the corpus
- Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images (2022, methodology): “The first step incorporates the acquisition of data in the form of MRI brain images from the Kaggle dataset [35].”
- Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, background): “Hence, accurate recognition and classification of brain tumors are critical for proper treatment [6].”
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, methodology): “It provides axial, coronal, and sagittal imaging for a more thorough evaluation [20].”