A Novel Framework for Brain Tumor Segmentation using Neuro Trypetidae Fruit Fly-Based UNet
Keywords:
MRI brain tumour pictures, feature extraction, tumour segmentation, tumour tracking, Dice and Jaccard, Trypetidae fruit fly optimization, UNet deep learningAbstract
Most challenging tasks associated with medical image processing involve segmenting and analyzing complex images, such as brain images. Additionally, MRI scans are frequently used to forecast many brain ailments; if the scans are complicated, the disease forecasting precision is relatively low. The current study has designed an approach to deal with this issue.to create a cutting-edge Trypetidae fruit fly-based UNet (TFFbU) system to precisely detect the tumour. Additionally, the UNet pooling module increased the Trypetidae fruit fly's fitness. That has typically produced the best outcomes. In the beginning, the system was trained using the standard datasets that are collected from the internet. As a result, the training errors are eliminated in the TFFbU's primary layer before the data has been cleaned of errors, which is then used to detect and segment tumours in the UNet dense layer. Lastly, the suggested model is run in MATLAB, and the effectiveness of the developed TFFbU. The model is estimated with various parameters like accuracy, recall, precision, Dice, and Jaccard. Additionally, the projected innovative TFFbU model has the capacity to segment and predict various tumour varieties.
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