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

• Published in: Scientific reports Vol. 14; no. 1; pp. 25800 - 29
• Main Authors: Akter, Sharmin, Simul Hasan Talukder, Md, Mondal, Sohag Kumar, Aljaidi, Mohammad, Bin Sulaiman, Rejwan, Alshammari, Ahmad Abdullah
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1857; 639/166/985; 639/705/117; Algorithms; Brain - diagnostic imaging; Brain - pathology; Brain cancer; Brain Neoplasms - classification; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Cancer; Decision making; DWT; ExtratreesClassifier; Glioma; Glioma - classification; Glioma - diagnostic imaging; Glioma - pathology; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; KNNImputer; Learning algorithms; Life expectancy; Life span; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Meningioma; Meningioma - diagnostic imaging; Meningioma - pathology; MRI; multidisciplinary; Neuroimaging; PCA; Pituitary; Pituitary gland; Science; Science (multidisciplinary); Statistical analysis; Statistical models; SVM; Tumors; DWT; XAI; MRI; LGBM; LR; SVM; KNNImputer; PCA; DT; RF; ExtratreesClassifier; GB; CB
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-74731-8

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.