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

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

<italic>Goal:</italic> Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. <italic>Methods:</italic> Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The dee...

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Bibliographic Details
Main Authors: M. V. S. Ramprasad, Md. Zia Ur Rahman, Masreshaw Demelash Bayleyegn
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Brain tumor segmentation
classification
feature extraction
deep learning convolutional neural network
robust edge analysis
Online Access:https://ieeexplore.ieee.org/document/9928545/

Internet

https://ieeexplore.ieee.org/document/9928545/

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