Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images-Reference-Cited by-同舟云学术

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

Published:2020-01 Issue: Volume:129 Page:181-189
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Sharif Muhammad Irfan,Li Jian Ping,Khan Muhammad Attique^ORCID,Saleem Muhammad Asim^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference49 articles.

1. A survey of MRI-based medical image analysis for brain tumor studies;Bauer;Phys. Med. Biol.,2013

2. Glioma segmentation with a unified algorithm in multimodal MRI images;Li;IEEE Access,2018

3. S. Alqazzaz, X. Sun, X. Yang, and L. Nokes, Automated brain tumor segmentation on multi-modal MR image using SegNet, Computational Visual Media, pp. 1–11.

4. Exciting new advances in neuro‐oncology: the avenue to a cure for malignant glioma;Van Meir;CA Cancer J. Clin.,2010

5. Big data analysis for brain tumor detection: deep convolutional neural networks;Amin;Fut. Gen. Comput. Syst.,2018

Cited by 192 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor;Expert Systems with Applications;2024-03

2. Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification;Expert Systems with Applications;2024-03

3. Self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for classification of brain tumor on MRI image;Biomedical Signal Processing and Control;2024-02

4. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology;Cancers;2024-01-10

5. A survey on cancer detection via convolutional neural networks: Current challenges and future directions;Neural Networks;2024-01