A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation-Reference-Cited by-同舟云学术

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A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation

Published:2022 Issue: Volume:3 Page:178-188
ISSN:2644-1276
Container-title:IEEE Open Journal of Engineering in Medicine and Biology
language:
Short-container-title:IEEE Open J. Eng. Med. Biol.

Author:

Ramprasad M. V. S.¹^ORCID,Rahman Md. Zia Ur²^ORCID,Bayleyegn Masreshaw Demelash³^ORCID

Affiliation:

1. Koneru Lakshmaiah Education Foundation, K L University, Guntur, India

2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, K L University, Vaddeswaram, Guntur, India

3. Center of Biomedical Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Ethiopia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782705/9698118/09928545.pdf?arnumber=9928545

Reference40 articles.

1. Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

2. Brain tumor classification using deep CNN features via transfer learning

3. A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain

4. A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

5. Medical image fusion based on laws of texture energy measures in stationary wavelet transform domain;padma;Int J Imag Syst Technol,2020

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1. Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model;Journal of Imaging Informatics in Medicine;2024-08-15

2. Artificial Intelligence-Based Brain Tumor Segmentation Using Adaptive Hybrid CNN and Classification by Multi-Scale Dilated MobileNet with Attention Mechanism for MRI Images;International Journal of Image and Graphics;2024-06-21

3. A Multiscale Atrous Convolution-based Adaptive ResUNet3 + with Attention-based ensemble convolution networks for brain tumour segmentation and classification using heuristic improvement;Biomedical Signal Processing and Control;2024-05

4. Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images;The Imaging Science Journal;2024-04-06

5. Feature selection using adaptive manta ray foraging optimization for brain tumor classification;Pattern Analysis and Applications;2024-03-07

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