An Adaptive Xception Model for Classification of Brain Tumors

Thakur, Arastu; Bhatia Khan, Surbhi; Palaiahnakote, Shivakumara; Kumar V, Vinoth; Almusharraf, Ahlam; Mashat, Arwa

Abstract

Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images of complex backgrounds, the proposed work aims at accurate classification of diverse types of brain tumors such that an appropriate model can be used for disease identification. This study considers glioma, meningioma, no tumor, and pituitary tumors for classification. To achieve an accurate classification, we explore the Xception architecture layer, which involves flattening, dropout, and dense layer operations. The model extracts features based on shapes, spatial relationships, and structure of the image, discriminating between the different brain tumor images. The model is evaluated on a dataset of 7023 MRI images for classification. The results of a large dataset and comparative study with the existing methods show that the proposed method is better than state-of-the-art in terms of classification rate. Specifically, our method achieves more than a 90% average classification rate, which is better than state-of-the-art. The results on noisy and blurred datasets show that the proposed model is robust to noise and blur.

Citation

Thakur, A., Bhatia Khan, S., Palaiahnakote, S., Kumar V, V., Almusharraf, A., & Mashat, A. (in press). An Adaptive Xception Model for Classification of Brain Tumors. International Journal of Pattern Recognition and Artificial Intelligence,

• Journal Article Type: Article
• Acceptance Date: Mar 22, 2024
• Deposit Date: Mar 22, 2024
• Print ISSN: 0218-0014
• Publisher: World Scientific Publishing
• Peer Reviewed: Peer Reviewed
• Keywords: Brain Tumor; Classification; MRI Imaging; Deep Learning; Xception Model; Neural Networks
• Publisher URL: http://www.worldscientific.com/worldscinet/ijprai