MRI Brain Tumor Identification and Classification Using Deep Learning Techniques.

Chellakh, Hafida; Moussaoui, Abdelouahab; Attia, Abdelouahab; Akhtar, Zahid

Deep learning has exponentially enhanced the state-of-the-art in several Artificial Intelligence (AI) domains, including computer vision, user authentication, healthcare, object recognition and image processing. Recently, deep rule-based classifier (DRB) is being employed to solve diverse problems of classification or prediction. Thus, in this paper, we present a novel, simple, automatic, and effective DRB classifier-based scheme for MRI brain tumor classification. The proposed framework is composed of three stages, i.e., preprocessing, feature extraction and classification. Especially, in the second stage, we have investigated and analyzed comparative performances of various deep features extracted by the AlexNet, VGG-16, ResNet-50, ResNet-18 deep learning networks. After feature extraction step, a DRB classifier is employed for classification. The proposed method is evaluated on two publicly available datasets that are available on Kaggel website. The first database is a binary database (i.e., tumor and no tumor). Whereas the second one is a multiclass database (i.e., Meningioma, Glioma and Pituitary tumor). Experimental results show that the proposed method can obtain notable performances. Moreover, the comparative study with classical methods (e.g., SVM, KNN, Decision tree) as well as several state-of-the-art distance techniques demonstrated the effectiveness of proposed approach in MRI brain tumor detection and classification.

BRAIN tumor diagnosis; DEEP learning; MAGNETIC resonance imaging of the brain

Ingénierie des Systèmes d'Information, 2023, Vol 28, Issue 1, p13

1633-1311

Academic Journal

10.18280/isi.280102