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Silva Santana L, Borges Camargo Diniz J, Mothé Glioche Gasparri L, Buccaran Canto A, Batista Dos Reis S, Santana Neville Ribeiro I, Gadelha Figueiredo E, Paulo Mota Telles J. Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis. World Neurosurg 2024; 186:204-218.e2. [PMID: 38580093 DOI: 10.1016/j.wneu.2024.03.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types. METHODS A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity. RESULTS Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00). CONCLUSIONS ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.
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Affiliation(s)
| | | | | | | | | | - Iuri Santana Neville Ribeiro
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Eberval Gadelha Figueiredo
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - João Paulo Mota Telles
- Department of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
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Li L, Yang J, Por LY, Khan MS, Hamdaoui R, Hussain L, Iqbal Z, Rotaru IM, Dobrotă D, Aldrdery M, Omar A. Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques. Heliyon 2024; 10:e26192. [PMID: 38404820 PMCID: PMC10884486 DOI: 10.1016/j.heliyon.2024.e26192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/30/2024] [Accepted: 02/08/2024] [Indexed: 02/27/2024] Open
Abstract
Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
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Affiliation(s)
- Liangyu Li
- Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
- Health Informatics Laboratory, Cancer Research Institute, Chifeng Cancer Hospital (Second Affiliated Hospital of Chifeng University), Medical Department, Chifeng University, Chifeng City, Inner Mongolia Autonomous Region, 024000, China
| | - Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lip Yee Por
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Mohammad Shahbaz Khan
- Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, United States
| | - Rim Hamdaoui
- Department of Computer Science, College of Science and Human Studies Dawadmi, Shaqra University, Shaqra, Riyadh, Saudi Arabia
| | - Lal Hussain
- Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
- Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
| | - Zahoor Iqbal
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
| | - Ionela Magdalena Rotaru
- Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
| | - Dan Dobrotă
- Faculty of Engineering, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
| | - Moutaz Aldrdery
- Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia
| | - Abdulfattah Omar
- Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Saudi Arabia
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Hussain L, Malibari AA, Alzahrani JS, Alamgeer M, Obayya M, Al-Wesabi FN, Mohsen H, Hamza MA. Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI. Sci Rep 2022; 12:15389. [PMID: 36100621 PMCID: PMC9470580 DOI: 10.1038/s41598-022-19563-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractAccurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
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