CAHO-DNFN: ME-Net-based segmentation and optimized deep neuro fuzzy network for brain tumour classification with MRI-Reference-Cited by-同舟云学术

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CAHO-DNFN: ME-Net-based segmentation and optimized deep neuro fuzzy network for brain tumour classification with MRI

Published:2023-05-20 Issue: Volume: Page:1-19
ISSN:1368-2199
Container-title:The Imaging Science Journal
language:en
Short-container-title:The Imaging Science Journal

Author:

Neelima G.¹,Satish Aravapalli Rama²,Maram Balajee³,Chigurukota Dhanunjaya Rao⁴

Affiliation:

1. Department of Computer Science and Engineering, Vignan Institute of Information Technology (Autonomous), Visakhapatnam, India

2. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

3. Apex Institute Technology-Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India

4. Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, India

Publisher

Informa UK Limited

Subject

Computer Vision and Pattern Recognition,Media Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/13682199.2023.2211890

Reference50 articles.

1. Wu P, Chang Q. Brain tumor segmentation on multimodal 3D-MRI using deep learning method. In: 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2020, p. 635–639.

2. Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

3. Classification of Brain Tumor using Manta Ray Foraging Optimization-based DeepCNN Classifier

4. Hybrid classifier: Brain Tumor Classification and Segmentation using Genetic-based Grey Wolf optimization

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