Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

• Published in: Expert systems with applications Vol. 238; p. 122347
• Main Authors: Akter, Atika, Nosheen, Nazeela, Ahmed, Sabbir, Hossain, Mariom, Yousuf, Mohammad Abu, Almoyad, Mohammad Ali Abdullah, Hasan, Khondokar Fida, Moni, Mohammad Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Brain tumor; Classification of brain MRI; Deep convolutional neural network; Machine learning; Segmentation; Brain tumor; Segmentation; Deep convolutional neural network; Classification of brain MRI; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.122347

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain examination and advances in AI-based detection methods, building an accurate and efficient model for detecting and categorizing tumors from MRI images remains a challenge. To address this problem, we proposed a deep Convolutional Neural Network (CNN)-based architecture for automatic brain image classification into four classes and a U-Net-based segmentation model. Using six benchmarked datasets, we tested the classification model and trained the segmentation model, enabling side-by-side comparison of the impact of segmentation on tumor classification in brain MRI images. We also evaluated two classification methods based on accuracy, recall, precision, and AUC. Our developed novel deep learning-based model for brain tumor classification and segmentation outperforms existing pre-trained models across all six datasets. The results demonstrate that our classification model achieved the highest accuracy of 98.7% in a merged dataset and 98.8% with the segmentation approach, with the highest classification accuracy reaching 97.7% among the four individual datasets. Thus, this novel framework could be applicable in clinics for the automatic identification and segmentation of brain tumors utilizing MRI scan input images. •Brain tumor type depends on complex intercellular structures.•Classification of full brain MRI requires more time and resources.•Classification of segmented tumor images requires additional computational complexity.•Brain tumor classification into four classes (Glioma, meningioma, pituitary, no tumor).•CNN-based classification model and U-Net-based segmentation model implementation.