Paper Info
Title
Two-fold brain tumor segmentation using fuzzy image enhancement and DeepBrainet2.0
Abstract
Segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is a challenging and essential task for brain tumor detection. In this article, a new two-fold fuzzy and deep learning-based approach is proposed for the segmentation of three types of brain tumor. A total of eight CNN-based approaches are proposed, from the basic architecture (Brainet1.01) to the final architecture DeepBrainet2.0 to find out the optimal one for brain tumor segmentation. Brainet1.01 is upgraded to DeepBrainet2.0 via Brainet1.02 to Brainet1.04, Brainet2.0, Brainet3.0, and Deepbrainet1.0 by changing the number of layers, neurons, and type of connection between neurons. The best result (final brain tumor mask) is achieved by using DeepBrainet2.0 which utilizes an efficient skip-connection mapping plan to lean the brain tumor features. To make DeepBrainet2.0 efficient, enhanced train and test instances are created by utilizing a fuzzy-logic-based method. Also, a second augmented dataset is created which applies five types of augmentations to the images of the first dataset to convert the system into robust from the alteration in orientation, scale, and flip. The outcome of the proposed method is tested on three datasets where the accuracy rate obtained are 94.3%, 96.7%, and 95.2% which specifies the efficacy of the proposed approach.
Year
DOI
Venue
2022
10.1007/s11042-022-13014-8
Multimedia Tools and Applications
Keywords
DocType
Volume
Brain tumor segmentation, Fuzzy image enhancement, DeepBrainet2.0, Augmented dataset, Deep learning
Journal
81
Issue
ISSN
Citations 
21
1380-7501
0
PageRank 
References 
Authors
0.34
10
1
Name
Order
Citations
PageRank
Jyotismita Chaki100.34