| Autor: |
Neelima, G., Satish, Aravapalli Rama, Maram, Balajee, Chigurukota, Dhanunjaya Rao |
| Předmět: |
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| Zdroj: |
Imaging Science Journal; Jul2024, Vol. 72 Issue 5, p557-575, 19p |
| Abstrakt: |
A brain tumour is a deadly syndrome caused due to abnormal and uncontrolled expansion of extra cells that creates several tissues in the brain to affect the nervous system. It rapidly increases the growth of tumour cells and affects the brain by damaging or squeezing healthy tissues. Automatic brain tumour classification was done by conditional aquila horse herd optimization driven deep neuro fuzzy network (CAHO-based DNFN) based on MR image. First, image segmentation is done with a multi-encoder net framework (ME-Net), and features that involve statistical and convolutional neural network (CNN) features are extracted. Then, the ME-Net training is performed using AHO. Utilizing deep neuro fuzzy network (DNFN), which is trained by fusing CAViaR with AO and HOA, tumour classification is carried out utilizing augmented data after the process. The proposed scheme showed outstanding results with the measures, namely testing accuracy, specificity, and sensitivity that acquired the values of 0.915, 0.9 and 0.926, respectively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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