Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.
Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis t...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 5; pp. 2793 - 2831 |
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| Main Authors: | , , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188953408&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188953408 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Oct2025 vid: 38 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188953408 188953408 189894382 188953408 10.1007/s10278-024-01368-4 188953408 ppf: 2793 ppct: 38 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI. aug: au: Tandel, Gopal Singh Tiwari, Ashish Kakde, Omprakash G. affil: https://ror.org/03vrx7m55 Department of Computer Science, Allahabad Degree College, University of Allahabad, Prayagraj, India sug: subj: Brain Neoplasms Diagnosis Neoplasm Grading Methods Neoplasm Grading Classification Brain Neoplasms Classification Detection Algorithms Cost Effectiveness Analysis Magnetic Resonance Imaging Methods Diagnosis, Computer Assisted Methods Sensitivity and Specificity Evaluation Machine Learning Algorithms Artificial Intelligence Image Processing, Computer Assisted Noninvasive Procedures Diagnosis, Computer Assisted Economics Human Support Vector Machine Decision Trees Discriminant Analysis Neural Networks (Computer) Random Forest Descriptive Statistics Automation Prediction Models Adolescence Adult Middle Age Aged Aged, 80 and Over Cancer Patients Prediction Algorithms Brain Neoplasms Pathology Protocols Data Analysis Software Brain Neoplasms Prognosis Image Interpretation, Computer Assisted Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades. Our system employs ensemble deep learning (EDL) within an MRI multiclass framework that includes five datasets: two-class (C2), three-class (C3), four-class (C4), five-class (C5) and six-class (C6). The EDL utilizes a majority voting algorithm to classify brain tumors by combining seven renowned deep learning (DL) models—EfficientNet, VGG16, ResNet18, GoogleNet, ResNet50, Inception-V3 and DarkNet—and seven machine learning (ML) models, including support vector machine, K-nearest neighbour, Naïve Bayes, decision tree, linear discriminant analysis, artificial neural network and random forest. Additionally, local interpretable model-agnostic explanations (LIME) are employed as an explainable AI algorithm, providing a visual representation of the CNN's internal workings to enhance the credibility of the results. Through extensive five-fold cross-validation experiments, the DL-based majority voting algorithm outperformed the ML-based majority voting algorithm, achieving the highest average accuracies of 100 ± 0.00%, 98.55 ± 0.35%, 98.47 ± 0.63%, 95.34 ± 1.17% and 96.61 ± 0.85% for the C2, C3, C4, C5 and C6 datasets, respectively. Majority voting algorithms typically yield consistent results across different folds of the brain tumor data and enhance performance compared to any individual deep learning and machine learning models. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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