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Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis

Today, Magnetic Resonance Imaging (MRI) is a prominent technique used in medicine, produces a significant and varied range of tissue contrasts in each imaging modalities, and is frequently employed by medical professionals to identify brain malignancies. With brain tumor being a very deadly disease,...

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Autores principales: Mgbejime, Goodness Temofe, Hossin, Md Altab, Nneji, Grace Ugochi, Monday, Happy Nkanta, Ekong, Favour
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600759/
https://www.ncbi.nlm.nih.gov/pubmed/36292173
http://dx.doi.org/10.3390/diagnostics12102484
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author Mgbejime, Goodness Temofe
Hossin, Md Altab
Nneji, Grace Ugochi
Monday, Happy Nkanta
Ekong, Favour
author_facet Mgbejime, Goodness Temofe
Hossin, Md Altab
Nneji, Grace Ugochi
Monday, Happy Nkanta
Ekong, Favour
author_sort Mgbejime, Goodness Temofe
collection PubMed
description Today, Magnetic Resonance Imaging (MRI) is a prominent technique used in medicine, produces a significant and varied range of tissue contrasts in each imaging modalities, and is frequently employed by medical professionals to identify brain malignancies. With brain tumor being a very deadly disease, early detection will help increase the likelihood that the patient will receive the appropriate medical care leading to either a full elimination of the tumor or the prolongation of the patient’s life. However, manually examining the enormous volume of magnetic resonance imaging (MRI) images and identifying a brain tumor or cancer is extremely time-consuming and requires the expertise of a trained medical expert or brain doctor to manually detect and diagnose brain cancer using multiple Magnetic Resonance images (MRI) with various modalities. Due to this underlying issue, there is a growing need for increased efforts to automate the detection and diagnosis process of brain tumor without human intervention. Another major concern most research articles do not consider is the low quality nature of MRI images which can be attributed to noise and artifacts. This article presents a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to precisely handle the problem of low quality MRI images by eliminating noisy elements and enhancing the visible trainable features of the image. The enhanced image is then fed to the proposed PCNN to learn the features and classify the tumor using sigmoid classifier. To properly train the model, a publicly available dataset is collected and utilized for this research. Additionally, different optimizers and different values of dropout and learning rates are used in the course of this study. The proposed PCNN with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm achieved an accuracy of 98.7%, sensitivity of 99.7%, and specificity of 97.4%. In comparison with other state-of-the-art brain tumor methods and pre-trained deep transfer learning models, the proposed PCNN model obtained satisfactory performance.
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spelling pubmed-96007592022-10-27 Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis Mgbejime, Goodness Temofe Hossin, Md Altab Nneji, Grace Ugochi Monday, Happy Nkanta Ekong, Favour Diagnostics (Basel) Article Today, Magnetic Resonance Imaging (MRI) is a prominent technique used in medicine, produces a significant and varied range of tissue contrasts in each imaging modalities, and is frequently employed by medical professionals to identify brain malignancies. With brain tumor being a very deadly disease, early detection will help increase the likelihood that the patient will receive the appropriate medical care leading to either a full elimination of the tumor or the prolongation of the patient’s life. However, manually examining the enormous volume of magnetic resonance imaging (MRI) images and identifying a brain tumor or cancer is extremely time-consuming and requires the expertise of a trained medical expert or brain doctor to manually detect and diagnose brain cancer using multiple Magnetic Resonance images (MRI) with various modalities. Due to this underlying issue, there is a growing need for increased efforts to automate the detection and diagnosis process of brain tumor without human intervention. Another major concern most research articles do not consider is the low quality nature of MRI images which can be attributed to noise and artifacts. This article presents a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to precisely handle the problem of low quality MRI images by eliminating noisy elements and enhancing the visible trainable features of the image. The enhanced image is then fed to the proposed PCNN to learn the features and classify the tumor using sigmoid classifier. To properly train the model, a publicly available dataset is collected and utilized for this research. Additionally, different optimizers and different values of dropout and learning rates are used in the course of this study. The proposed PCNN with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm achieved an accuracy of 98.7%, sensitivity of 99.7%, and specificity of 97.4%. In comparison with other state-of-the-art brain tumor methods and pre-trained deep transfer learning models, the proposed PCNN model obtained satisfactory performance. MDPI 2022-10-13 /pmc/articles/PMC9600759/ /pubmed/36292173 http://dx.doi.org/10.3390/diagnostics12102484 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
Mgbejime, Goodness Temofe
Hossin, Md Altab
Nneji, Grace Ugochi
Monday, Happy Nkanta
Ekong, Favour
Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title_full Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title_fullStr Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title_full_unstemmed Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title_short Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis
title_sort parallelistic convolution neural network approach for brain tumor diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600759/
https://www.ncbi.nlm.nih.gov/pubmed/36292173
http://dx.doi.org/10.3390/diagnostics12102484
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