Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.

Autor: Akter S; Biomedical Engineering, Jashore University of Science and Technology, Jashore, Bangladesh. sharmintalukder120@gmail.com., Simul Hasan Talukder M; Electrical and Electronic Engineering, Dhaka University of Engineering and Technology, Dhaka, Bangladesh. simul@duet.ac.bd., Mondal SK; Electrical and Electronic Engineering, Sohag Kumar Mondal, Khulna University of Engineering and Technology, Khulna, Bangladesh., Aljaidi M; Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan., Bin Sulaiman R; Rejwan Bin Sulaiman, School of Computer science and Technology, Northumbria University, Newcastle Upon Tyne, UK., Alshammari AA; Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 28; Vol. 14 (1), pp. 25800. Date of Electronic Publication: 2024 Oct 28.
DOI: 10.1038/s41598-024-74731-8
Abstrakt: Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model's predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.
(© 2024. The Author(s).)
Databáze: MEDLINE
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