Trends in DNN Model based Classification and Segmentation of Brain Tumor Detection
Pooja Kataria1, Ayush Dogra2, 3, Mili Gupta4, Tripti Sharma1, *, Bhawna Goyal1, *
Identifiers and Pagination:Year: 2023
E-location ID: e187444002303060
Publisher ID: e187444002303060
Article History:Received Date: 06/09/2022
Revision Received Date: 08/02/2023
Acceptance Date: 22/02/2023
Electronic publication date: 06/04/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterizing a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival. Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancements. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the population growth.
This article presents a comprehensive evaluation of conventional machine learning models and evolving deep learning techniques for brain tumor segmentation and classification.
In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping require redundancy correction, the input data training needs to be more exhaustive, and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection and cancers.