A deep learning approach for brain tumour detection system using convolutional neural networks. (15th December 2021)

• Record Type: Journal Article
• Authors: Kalaiselvi, T.; Padmapriya, S.T.; Sriramakrishnan, P.; Somasundaram, K.
• Is Part Of: International journal of dynamical systems and differential equations. Volume 11:Number 5/6(2021)
• Page Start: 514
• Page End: 526
• Publication Date: 2021-12-15
• Subjects: neural networks -- MRI -- magnetic resonance imaging -- brain tumour -- deep learning -- tumour detection -- CNN -- convolutional neural network -- BraTS Dataset -- activation functions -- WBA datasets; Differential equations -- Periodicals; 515.35
• Languages: English
• ISSNs: 1752-3583

Abstract

The proposed work is aimed to develop convolutional neural network (CNN) architecture based computer aided diagnostic system for human brain tumour detection process from magnetic resonance imaging (MRI) volumes. CNN is a class of deep learning networks that are commonly applied to analyse voluminous images. In the proposed method, a CNN model is constructed and trained using MRI volumes of BraTS2013 data. More than 4000 images of normal and tumour slices are used to train the proposed CNN system with 2-layers. The system is tested with about 1000 slices from BraTS and got very accurate results about 90-98% of accuracy. Further, the performance of proposed CNN system is tested by taking a set of clinical MRI volumes of popular hospital. The obtained results are discussed and focused for the future improvement of the proposed system.