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Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

التفاصيل البيبلوغرافية
العنوان: Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
المؤلفون: Vinayak Singh, Mahendra Kumar Gourisaria, Harshvardhan GM, Siddharth Swarup Rautaray, Manjusha Pandey, Manoj Sahni, Ernesto Leon-Castro, Luis F. Espinoza-Audelo
المصدر: Applied Sciences, Vol 12, Iss 6, p 2900 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: convolutional neural network (CNN), machine learning (ML), deep learning, artificial neural network (ANN), brain tumor, medical imaging, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/12/6/2900; https://doaj.org/toc/2076-3417
DOI: 10.3390/app12062900
URL الوصول: https://doaj.org/article/6ab23d62d47841d0a8bfaded510cbab3
رقم الأكسشن: edsdoj.6ab23d62d47841d0a8bfaded510cbab3
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app12062900