An effective ensemble learning approach for classification of glioma grades based on novel MRI features

• Published in: Scientific reports Vol. 14; no. 1; pp. 11977 - 14
• Main Authors: Hassan, Mohammed Falih, Al-Zurfi, Ahmed Naser, Alsalihi, Mohammed Hamzah, Ahmed, Khandakar
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; Algorithms; Artificial intelligence; Brain cancer; Brain Neoplasms - classification; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Classification; Diagnosis; Ensemble learning; Glioma; Glioma - classification; Glioma - diagnostic imaging; Glioma - pathology; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; multidisciplinary; Neoplasm Grading; Novel MRI features; Science; Science (multidisciplinary); Statistical analysis; Tumor classification; Tumors; Novel MRI features; Ensemble learning; Brain tumors; Tumor classification; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-61444-1

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.