Cargando…
A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143310/ https://www.ncbi.nlm.nih.gov/pubmed/33919358 http://dx.doi.org/10.3390/diagnostics11050744 |
_version_ | 1783696724045332480 |
---|---|
author | Masood, Momina Nazir, Tahira Nawaz, Marriam Mehmood, Awais Rashid, Junaid Kwon, Hyuk-Yoon Mahmood, Toqeer Hussain, Amir |
author_facet | Masood, Momina Nazir, Tahira Nawaz, Marriam Mehmood, Awais Rashid, Junaid Kwon, Hyuk-Yoon Mahmood, Toqeer Hussain, Amir |
author_sort | Masood, Momina |
collection | PubMed |
description | A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches. |
format | Online Article Text |
id | pubmed-8143310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81433102021-05-25 A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images Masood, Momina Nazir, Tahira Nawaz, Marriam Mehmood, Awais Rashid, Junaid Kwon, Hyuk-Yoon Mahmood, Toqeer Hussain, Amir Diagnostics (Basel) Article A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches. MDPI 2021-04-21 /pmc/articles/PMC8143310/ /pubmed/33919358 http://dx.doi.org/10.3390/diagnostics11050744 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Masood, Momina Nazir, Tahira Nawaz, Marriam Mehmood, Awais Rashid, Junaid Kwon, Hyuk-Yoon Mahmood, Toqeer Hussain, Amir A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title | A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title_full | A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title_fullStr | A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title_full_unstemmed | A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title_short | A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images |
title_sort | novel deep learning method for recognition and classification of brain tumors from mri images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143310/ https://www.ncbi.nlm.nih.gov/pubmed/33919358 http://dx.doi.org/10.3390/diagnostics11050744 |
work_keys_str_mv | AT masoodmomina anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT nazirtahira anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT nawazmarriam anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT mehmoodawais anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT rashidjunaid anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT kwonhyukyoon anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT mahmoodtoqeer anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT hussainamir anoveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT masoodmomina noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT nazirtahira noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT nawazmarriam noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT mehmoodawais noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT rashidjunaid noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT kwonhyukyoon noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT mahmoodtoqeer noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages AT hussainamir noveldeeplearningmethodforrecognitionandclassificationofbraintumorsfrommriimages |