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Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model
Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagno...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/ https://www.ncbi.nlm.nih.gov/pubmed/36292230 http://dx.doi.org/10.3390/diagnostics12102541 |
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author | Samee, Nagwan Abdel Mahmoud, Noha F. Atteia, Ghada Abdallah, Hanaa A. Alabdulhafith, Maali Al-Gaashani, Mehdhar S. A. M. Ahmad, Shahab Muthanna, Mohammed Saleh Ali |
author_facet | Samee, Nagwan Abdel Mahmoud, Noha F. Atteia, Ghada Abdallah, Hanaa A. Alabdulhafith, Maali Al-Gaashani, Mehdhar S. A. M. Ahmad, Shahab Muthanna, Mohammed Saleh Ali |
author_sort | Samee, Nagwan Abdel |
collection | PubMed |
description | Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%). |
format | Online Article Text |
id | pubmed-9600529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96005292022-10-27 Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model Samee, Nagwan Abdel Mahmoud, Noha F. Atteia, Ghada Abdallah, Hanaa A. Alabdulhafith, Maali Al-Gaashani, Mehdhar S. A. M. Ahmad, Shahab Muthanna, Mohammed Saleh Ali Diagnostics (Basel) Article Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%). MDPI 2022-10-20 /pmc/articles/PMC9600529/ /pubmed/36292230 http://dx.doi.org/10.3390/diagnostics12102541 Text en © 2022 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 Samee, Nagwan Abdel Mahmoud, Noha F. Atteia, Ghada Abdallah, Hanaa A. Alabdulhafith, Maali Al-Gaashani, Mehdhar S. A. M. Ahmad, Shahab Muthanna, Mohammed Saleh Ali Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title | Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title_full | Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title_fullStr | Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title_full_unstemmed | Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title_short | Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model |
title_sort | classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600529/ https://www.ncbi.nlm.nih.gov/pubmed/36292230 http://dx.doi.org/10.3390/diagnostics12102541 |
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